This document summarizes a talk given by Ian Gent about gender balance in computer science. It discusses how unconscious biases can negatively impact women in the field. Through examples and images, it illustrates common stereotypes associated with gender roles. It also shares statistics demonstrating the lack of gender diversity among computer science faculty and professors. Finally, it provides recommendations for creating a more inclusive environment, such as avoiding biased language and disrespecting others. The overall message is that computer science would benefit from embracing people from all backgrounds.
Welche Karrierechancen bietet eine Werbeagentur für Screendesigner, Programmierer und Co.? Und was macht ein Webworker eigentlich den ganzen Tag? Erste Eindrücke gibt dieser Kurzvortrag!
A computer scientist has knowledge of computer science theory rather than hardware. They work on tasks like software engineering, programming, and computer graphics. Computer scientists earn an average salary of $90,000 USD and require at least a 3-year computer science degree along with 3 computer science credits to qualify for jobs in the field. Common education paths include a degree from Herzing College, University of Waterloo, or University of Toronto.
Alan Turing was a British mathematician and computer scientist who made fundamental contributions to computer science and artificial intelligence. He introduced the concept of a universal machine that could calculate algorithms and helped develop early computers. During World War II, Turing worked at Bletchley Park where he played a pivotal role in breaking German codes and shortening the war. Later in life, Turing was prosecuted for his homosexuality which was illegal at the time. He tragically committed suicide in 1954 at the age of 41.
This document contains the text of a presentation by Hansruedi Tremp comparing the encoding of information in computers and DNA from the perspective of a computer scientist. The presentation covers:
1) How information is encoded in binary (0s and 1s) on computer hard disks and memory sticks.
2) How DNA also uses a 4-symbol encoding scheme on nucleotides to store vast amounts of information in the nucleus of cells.
3) How this DNA information is used via transcription and translation to build proteins according to the genetic code, similar to how computer programs produce desired outputs.
Mathai Joseph, Advisor, Tata Consultancy Service discusses about Alan Turing at the Grand Launch of Alan Turing Centenary Celebrations at Persistent Systems
This document discusses systems biology and some of its tools. It defines systems biology as the study of interactions between parts of biological systems to understand how they function. Biological networks involve interactions between pathways. Networks can be modeled as nodes and edges. Tools described for modeling and analyzing networks include Cytoscape for visualization, CellDesigner for drawing networks, and STRING for protein-protein interaction data. Databases of pathways, interactions and models are also listed.
Welche Karrierechancen bietet eine Werbeagentur für Screendesigner, Programmierer und Co.? Und was macht ein Webworker eigentlich den ganzen Tag? Erste Eindrücke gibt dieser Kurzvortrag!
A computer scientist has knowledge of computer science theory rather than hardware. They work on tasks like software engineering, programming, and computer graphics. Computer scientists earn an average salary of $90,000 USD and require at least a 3-year computer science degree along with 3 computer science credits to qualify for jobs in the field. Common education paths include a degree from Herzing College, University of Waterloo, or University of Toronto.
Alan Turing was a British mathematician and computer scientist who made fundamental contributions to computer science and artificial intelligence. He introduced the concept of a universal machine that could calculate algorithms and helped develop early computers. During World War II, Turing worked at Bletchley Park where he played a pivotal role in breaking German codes and shortening the war. Later in life, Turing was prosecuted for his homosexuality which was illegal at the time. He tragically committed suicide in 1954 at the age of 41.
This document contains the text of a presentation by Hansruedi Tremp comparing the encoding of information in computers and DNA from the perspective of a computer scientist. The presentation covers:
1) How information is encoded in binary (0s and 1s) on computer hard disks and memory sticks.
2) How DNA also uses a 4-symbol encoding scheme on nucleotides to store vast amounts of information in the nucleus of cells.
3) How this DNA information is used via transcription and translation to build proteins according to the genetic code, similar to how computer programs produce desired outputs.
Mathai Joseph, Advisor, Tata Consultancy Service discusses about Alan Turing at the Grand Launch of Alan Turing Centenary Celebrations at Persistent Systems
This document discusses systems biology and some of its tools. It defines systems biology as the study of interactions between parts of biological systems to understand how they function. Biological networks involve interactions between pathways. Networks can be modeled as nodes and edges. Tools described for modeling and analyzing networks include Cytoscape for visualization, CellDesigner for drawing networks, and STRING for protein-protein interaction data. Databases of pathways, interactions and models are also listed.
In this presentation its given an introduction about Data Science, Data Scientist role and features, and how Python ecosystem provides great tools for Data Science process (Obtain, Scrub, Explore, Model, Interpret).
For that, an attached IPython Notebook ( http://bit.ly/python4datascience_nb ) exemplifies the full process of a corporate network analysis, using Pandas, Matplotlib, Scikit-learn, Numpy and Scipy.
Donald Knuth is an American computer scientist, mathematician, and professor emeritus at Stanford University. He began writing "The Art of Computer Programming" in 1962, which is a comprehensive monograph that covers various programming algorithms and their analysis. The work is divided into multiple volumes that cover different aspects of computer programming such as fundamental algorithms, sorting and searching, and syntactic algorithms. In developing the book, Knuth also popularized the use of asymptotic notation or "Big O" notation to characterize the growth rate of functions. Frustrated with publishing tools at the time, he developed the TeX computer typesetting system, which later became known as LaTeX. Knuth is strongly opposed to software patents, arguing that ideas that should be easily
Im Kontext von IoT spielt die Gewinnung und Verarbeitung von großen Datenmengen, z.B. von Sensoren eine große Rolle. Die Rohdaten alleine machen aber noch lange keine smarten Systeme. Aus Daten werden Informationen aus Informationen wird Wissen und aus Wissen resultieren Entscheidungen - im besten Fall. Neben der technischen Herausforderungen im Umgang mit BigData rückt die „schlaue Auswertung" derselben (Digitale Analyse) immer mehr in den Vordergrund und zeigt die Grenzen des Könnens vieler Unternehmen auf. Kein Wunder also, dass dem Berufsbild des Data Scientisten eine wachsende Bedeutung zukommt. Nicht umsonst benannte das Harvard Business Review diesen als „The sexiest job of the 21st Century“.
Die Digital Analytics Assocations e.V. (DAA) treibt gezielt Fach- und Führungskräfte sowie Unternehmen die Professionalisierung von Digitalen Analysten und Data Scientists voran.
Frank Pörschmann, Mitglied des Vorstands des DAA e.V., erzählt in diesem Vortrag etwas über
- den Unterschied zwischen BigData, SmartData und Data Analytics
- Datenökonomie
- das Berufsbild des Data Scientist / Digitalen Analysten
- Aus- und Fortbildungsmöglichkeiten
Tutorial 1: Your First Science App - Araport Developer WorkshopVivek Krishnakumar
Slide deck pertaining to Tutorial 1 of the Araport Developer Workshop conducted at TACC, Austin TX on November 5, 2014.
Presented by Vivek Krishnakumar
Justin F. Brunelle is a computer scientist who works at The MITRE Corporation and received his BS and MS in computer science from Old Dominion University. He is currently pursuing his PhD in digital preservation from ODU under Dr. Nelson, focusing on ensuring web pages are archived over time. Previously he conducted research in serious games and intelligent tutoring systems.
Journal of Computational Systems Biology (JCSB) is an open access online journal which aims to publish peer reviewed research articles and short communications in all aspects of computational biology and bioinformatics. JCSB comprehend the broad spectrum of computational bioscience including biological databases and bioalgorithms.
Systems biology: Bioinformatics on complete biological systemLars Juhl Jensen
Systems biology uses mathematical modeling to study molecular networks and complete biological systems. It requires detailed knowledge of molecular interactions, which can be determined through various high-throughput interaction assays. However, interaction data from different databases may have varying quality and identifiers, so integrating this data requires resolving these issues. Natural language processing of literature can provide additional interaction data by recognizing named entities and extracting relations from text.
This document discusses Jupyter, an open-source tool for interactive data science and scientific computing. Jupyter allows for interactive exploration, development, and communication through code, equations, visualizations and narrative text. It supports over 50 programming languages and has found widespread adoption in academia and industry for individual and collaborative work across the entire workflow of a scientific idea from data collection to publication. The document outlines Jupyter's history and architecture, ecosystem of related projects, and future development plans to enhance collaboration and software engineering capabilities.
Apps for Science - Elsevier Developer Network Workshop 201102remko caprio
This presentation is an introduction into programming OpenSocial Gadgets for Science.
1. overview of apps
2. social networks
3. opensocial
4. SciVerse Platform
5. SciVerse APIs
6. Coding OpenSocial Gadgets for SciVerse
7. Resources
Systems biology - Understanding biology at the systems levelLars Juhl Jensen
The document discusses systems biology and its goal of understanding biology at the systems level. It explains that systems biology studies complete biological systems by integrating multiple types of high-throughput omics data and mathematical modeling. It provides examples of modeling the cell cycle and integrating gene expression, protein interaction, and genetic interaction networks to understand complex multi-layer regulation within biological systems. Interactive online databases are described that allow users to explore omics data, expand networks, and investigate relationships between biological entities and diseases.
Analytics meets Big Data – R/Python auf der Hadoop/Spark-PlattformRising Media Ltd.
Big Data verändert nicht nur die Unternehmens-IT fundamental, sondern auch die Arbeit des Analysten. Die klassischen Analysten sehen sich im Zuge des Wandels zu einer datengetriebenen Unternehmenskultur mit neuen Anforderungen und ungewohnten technologischen Plattformen konfrontiert. Sie müssen als Data Scientist fachliche Fragestellungen unter dem Aspekt der Big Data-Technologien umsetzen, visualisieren und aus den Daten Werte generieren. Anhand eines konkreten Use Cases, der Programmierung eines Recommender-Systems, zeigen wir Ansätze, wie sich die gewohnten Vorgehensweisen und Werkzeuge eines Analysten (namentlich R und Python) mit einer Big Data-Technologie (Spark) kombinieren lassen. Ziel ist es, dem Analysten den Einstieg in die Big Data-Welt zu erleichtern. Wir demonstrieren die Arbeit mit diesem Toolset an anschaulichen Beispielen in einem interaktiven Workshop-Format und laden zur Diskussion und Nachahmung dieser Vorgehensweise ein. Der Workshop richtet sich an Teilnehmer mit Grundkenntnissen aus den Bereichen analytische Methoden und Machine Learning sowie R oder Python. Der Workshop wird auf der Spark-Plattform durchgeführt. Zu Spark werden keine Kenntnisse vorausgesetzt.
Computational Approaches to Systems BiologyMike Hucka
Presentation given at the Sydney Computational Biologists meetup on 21 August 2013 (http://australianbioinformatics.net/past-events/2013/8/21/computational-approaches-to-systems-biology.html).
Computational Biology and BioinformaticsSharif Shuvo
Computational Biology and Bioinformatics is a rapidly developing multi-disciplinary field. The systematic achievement of data made possible by genomics and proteomics technologies has created a tremendous gap between available data and their biological interpretation.
Do you know what k-Means? Cluster-Analysen Harald Erb
Cluster-Analysen sind heute "Brot und Butter"-Analysetechniken mit Verfahren, die zur Entdeckung von Ähnlichkeitsstrukturen in (großen) Datenbeständen genutzt werden, mit dem Ziel neue Gruppen in den Daten zu identifizieren. Der K-Means-Algorithmus ist dabei einer der einfachsten und bekanntesten unüberwachten Lernverfahren, das in verschiedenen Machine Learning Aufgabenstellung einsetzbar ist. Zum Beispiel können abnormale Datenpunkte innerhalb eines großen Data Sets gefunden, Textdokumente oder Kunden¬segmente geclustert werden. Bei Datenanalysen kann die Anwendung von Cluster-Verfahren ein guter Einstieg sein bevor andere Klassifikations- oder Regressionsmethoden zum Einsatz kommen.
In diesem Talk wird der K-Means Algorithmus samt Erweiterungen und Varianten nicht im Detail betrachtet und ist stattdessen eher als ein Platzhalter für andere Advanced Analytics-Verfahren zu verstehen, die heute „intelligente“ Bestandteile in modernen Softwarelösungen sind bzw. damit kombiniert werden können. Anhand von zwei Kurzbeispielen wird live gezeigt: (1) Identifizierung von Kunden-Cluster mit einem Big Data Discovery Tool und Python (Jupyter Notebook) und (2) die Realisierung einer Anomalieerkennung direkt im Echtzeitdatenstrom mit einer Stream Analytics Lösung von Oracle.
The Computer Scientist and the Cleaner v5turingfan
This document is a draft talk by Ian Gent about gender balance in computer science. It discusses how subtle biases can negatively impact women in the field. It presents an experiment that showed science faculty viewed identical resumes of male and female students differently, rating the male students as more competent and hireable. The document advocates for increasing gender diversity in computing, noting that subtle biases still exist today and excluding women deprives the field of valuable talent. It uses an analogy of difficulty settings in games to illustrate how gender and racial biases can compound, making success harder for women and minorities.
The Computer Scientist and the Cleaner v3turingfan
This document summarizes a talk given by Ian Gent about gender balance and sexism in computer science. The talk uses examples like an ambiguous story about "the computer scientist and the cleaner" to show inherent biases people have in associating certain roles with gender. It discusses the lack of gender balance among computer science faculty and leaders historically at the University of St Andrews. The talk urges the audience to make computer science a welcoming place for women by not being sexist, using sexist language, or dismissing concerns about sexism. It promotes the idea of men being allies for gender equality.
This document is a transcript of a talk given by Ian Gent about gender balance and inclusiveness in computer science. The talk discusses how subtle biases can negatively impact women in the field. It describes an experiment that found science faculty judged identical resumes more positively when the applicant was male rather than female. The talk advocates for improving gender balance in computer science because it is fair and would make the field better by drawing from the talents of all people. It notes the lack of women historically in faculty roles at the University of St Andrews and argues that subtle biases still present a problem for gender equality in science today.
In this presentation its given an introduction about Data Science, Data Scientist role and features, and how Python ecosystem provides great tools for Data Science process (Obtain, Scrub, Explore, Model, Interpret).
For that, an attached IPython Notebook ( http://bit.ly/python4datascience_nb ) exemplifies the full process of a corporate network analysis, using Pandas, Matplotlib, Scikit-learn, Numpy and Scipy.
Donald Knuth is an American computer scientist, mathematician, and professor emeritus at Stanford University. He began writing "The Art of Computer Programming" in 1962, which is a comprehensive monograph that covers various programming algorithms and their analysis. The work is divided into multiple volumes that cover different aspects of computer programming such as fundamental algorithms, sorting and searching, and syntactic algorithms. In developing the book, Knuth also popularized the use of asymptotic notation or "Big O" notation to characterize the growth rate of functions. Frustrated with publishing tools at the time, he developed the TeX computer typesetting system, which later became known as LaTeX. Knuth is strongly opposed to software patents, arguing that ideas that should be easily
Im Kontext von IoT spielt die Gewinnung und Verarbeitung von großen Datenmengen, z.B. von Sensoren eine große Rolle. Die Rohdaten alleine machen aber noch lange keine smarten Systeme. Aus Daten werden Informationen aus Informationen wird Wissen und aus Wissen resultieren Entscheidungen - im besten Fall. Neben der technischen Herausforderungen im Umgang mit BigData rückt die „schlaue Auswertung" derselben (Digitale Analyse) immer mehr in den Vordergrund und zeigt die Grenzen des Könnens vieler Unternehmen auf. Kein Wunder also, dass dem Berufsbild des Data Scientisten eine wachsende Bedeutung zukommt. Nicht umsonst benannte das Harvard Business Review diesen als „The sexiest job of the 21st Century“.
Die Digital Analytics Assocations e.V. (DAA) treibt gezielt Fach- und Führungskräfte sowie Unternehmen die Professionalisierung von Digitalen Analysten und Data Scientists voran.
Frank Pörschmann, Mitglied des Vorstands des DAA e.V., erzählt in diesem Vortrag etwas über
- den Unterschied zwischen BigData, SmartData und Data Analytics
- Datenökonomie
- das Berufsbild des Data Scientist / Digitalen Analysten
- Aus- und Fortbildungsmöglichkeiten
Tutorial 1: Your First Science App - Araport Developer WorkshopVivek Krishnakumar
Slide deck pertaining to Tutorial 1 of the Araport Developer Workshop conducted at TACC, Austin TX on November 5, 2014.
Presented by Vivek Krishnakumar
Justin F. Brunelle is a computer scientist who works at The MITRE Corporation and received his BS and MS in computer science from Old Dominion University. He is currently pursuing his PhD in digital preservation from ODU under Dr. Nelson, focusing on ensuring web pages are archived over time. Previously he conducted research in serious games and intelligent tutoring systems.
Journal of Computational Systems Biology (JCSB) is an open access online journal which aims to publish peer reviewed research articles and short communications in all aspects of computational biology and bioinformatics. JCSB comprehend the broad spectrum of computational bioscience including biological databases and bioalgorithms.
Systems biology: Bioinformatics on complete biological systemLars Juhl Jensen
Systems biology uses mathematical modeling to study molecular networks and complete biological systems. It requires detailed knowledge of molecular interactions, which can be determined through various high-throughput interaction assays. However, interaction data from different databases may have varying quality and identifiers, so integrating this data requires resolving these issues. Natural language processing of literature can provide additional interaction data by recognizing named entities and extracting relations from text.
This document discusses Jupyter, an open-source tool for interactive data science and scientific computing. Jupyter allows for interactive exploration, development, and communication through code, equations, visualizations and narrative text. It supports over 50 programming languages and has found widespread adoption in academia and industry for individual and collaborative work across the entire workflow of a scientific idea from data collection to publication. The document outlines Jupyter's history and architecture, ecosystem of related projects, and future development plans to enhance collaboration and software engineering capabilities.
Apps for Science - Elsevier Developer Network Workshop 201102remko caprio
This presentation is an introduction into programming OpenSocial Gadgets for Science.
1. overview of apps
2. social networks
3. opensocial
4. SciVerse Platform
5. SciVerse APIs
6. Coding OpenSocial Gadgets for SciVerse
7. Resources
Systems biology - Understanding biology at the systems levelLars Juhl Jensen
The document discusses systems biology and its goal of understanding biology at the systems level. It explains that systems biology studies complete biological systems by integrating multiple types of high-throughput omics data and mathematical modeling. It provides examples of modeling the cell cycle and integrating gene expression, protein interaction, and genetic interaction networks to understand complex multi-layer regulation within biological systems. Interactive online databases are described that allow users to explore omics data, expand networks, and investigate relationships between biological entities and diseases.
Analytics meets Big Data – R/Python auf der Hadoop/Spark-PlattformRising Media Ltd.
Big Data verändert nicht nur die Unternehmens-IT fundamental, sondern auch die Arbeit des Analysten. Die klassischen Analysten sehen sich im Zuge des Wandels zu einer datengetriebenen Unternehmenskultur mit neuen Anforderungen und ungewohnten technologischen Plattformen konfrontiert. Sie müssen als Data Scientist fachliche Fragestellungen unter dem Aspekt der Big Data-Technologien umsetzen, visualisieren und aus den Daten Werte generieren. Anhand eines konkreten Use Cases, der Programmierung eines Recommender-Systems, zeigen wir Ansätze, wie sich die gewohnten Vorgehensweisen und Werkzeuge eines Analysten (namentlich R und Python) mit einer Big Data-Technologie (Spark) kombinieren lassen. Ziel ist es, dem Analysten den Einstieg in die Big Data-Welt zu erleichtern. Wir demonstrieren die Arbeit mit diesem Toolset an anschaulichen Beispielen in einem interaktiven Workshop-Format und laden zur Diskussion und Nachahmung dieser Vorgehensweise ein. Der Workshop richtet sich an Teilnehmer mit Grundkenntnissen aus den Bereichen analytische Methoden und Machine Learning sowie R oder Python. Der Workshop wird auf der Spark-Plattform durchgeführt. Zu Spark werden keine Kenntnisse vorausgesetzt.
Computational Approaches to Systems BiologyMike Hucka
Presentation given at the Sydney Computational Biologists meetup on 21 August 2013 (http://australianbioinformatics.net/past-events/2013/8/21/computational-approaches-to-systems-biology.html).
Computational Biology and BioinformaticsSharif Shuvo
Computational Biology and Bioinformatics is a rapidly developing multi-disciplinary field. The systematic achievement of data made possible by genomics and proteomics technologies has created a tremendous gap between available data and their biological interpretation.
Do you know what k-Means? Cluster-Analysen Harald Erb
Cluster-Analysen sind heute "Brot und Butter"-Analysetechniken mit Verfahren, die zur Entdeckung von Ähnlichkeitsstrukturen in (großen) Datenbeständen genutzt werden, mit dem Ziel neue Gruppen in den Daten zu identifizieren. Der K-Means-Algorithmus ist dabei einer der einfachsten und bekanntesten unüberwachten Lernverfahren, das in verschiedenen Machine Learning Aufgabenstellung einsetzbar ist. Zum Beispiel können abnormale Datenpunkte innerhalb eines großen Data Sets gefunden, Textdokumente oder Kunden¬segmente geclustert werden. Bei Datenanalysen kann die Anwendung von Cluster-Verfahren ein guter Einstieg sein bevor andere Klassifikations- oder Regressionsmethoden zum Einsatz kommen.
In diesem Talk wird der K-Means Algorithmus samt Erweiterungen und Varianten nicht im Detail betrachtet und ist stattdessen eher als ein Platzhalter für andere Advanced Analytics-Verfahren zu verstehen, die heute „intelligente“ Bestandteile in modernen Softwarelösungen sind bzw. damit kombiniert werden können. Anhand von zwei Kurzbeispielen wird live gezeigt: (1) Identifizierung von Kunden-Cluster mit einem Big Data Discovery Tool und Python (Jupyter Notebook) und (2) die Realisierung einer Anomalieerkennung direkt im Echtzeitdatenstrom mit einer Stream Analytics Lösung von Oracle.
The Computer Scientist and the Cleaner v5turingfan
This document is a draft talk by Ian Gent about gender balance in computer science. It discusses how subtle biases can negatively impact women in the field. It presents an experiment that showed science faculty viewed identical resumes of male and female students differently, rating the male students as more competent and hireable. The document advocates for increasing gender diversity in computing, noting that subtle biases still exist today and excluding women deprives the field of valuable talent. It uses an analogy of difficulty settings in games to illustrate how gender and racial biases can compound, making success harder for women and minorities.
The Computer Scientist and the Cleaner v3turingfan
This document summarizes a talk given by Ian Gent about gender balance and sexism in computer science. The talk uses examples like an ambiguous story about "the computer scientist and the cleaner" to show inherent biases people have in associating certain roles with gender. It discusses the lack of gender balance among computer science faculty and leaders historically at the University of St Andrews. The talk urges the audience to make computer science a welcoming place for women by not being sexist, using sexist language, or dismissing concerns about sexism. It promotes the idea of men being allies for gender equality.
This document is a transcript of a talk given by Ian Gent about gender balance and inclusiveness in computer science. The talk discusses how subtle biases can negatively impact women in the field. It describes an experiment that found science faculty judged identical resumes more positively when the applicant was male rather than female. The talk advocates for improving gender balance in computer science because it is fair and would make the field better by drawing from the talents of all people. It notes the lack of women historically in faculty roles at the University of St Andrews and argues that subtle biases still present a problem for gender equality in science today.
The computer scientist and the cleaner is a parable about gender preconceptions.
These slides are a draft talk to first year students on gender balance and sexism in Computer Science.
For more details about the context of this talk, visit my blog at
http://iangent.blogspot.co.uk/2013/10/the-computer-scientist-and-cleaner.html
Women in Science 2015: The Computer Scientist and the Cleanerturingfan
If I tell you a story about a heterosexual couple who are a computer scientist and a cleaner, would you have any preconceptions as to which was the man and which was the woman? You might not, but Google image searches show that the internet does.
Prof Ian Gent will use this as a parable about the vital problem of gender imbalance and stereotyping in Computer Science, perhaps the most important problem for the field. Ian will also discuss the Petrie Multiplier, a graphic illustration of how gender imbalance can dramatically affect the minority, even when the majority doesn't behave any worse.
Talk given at the Dundee Women in Science Festival, 18 March 2015
Fitting in CS when the stereotypes don't fit with Colleen LewisDatabricks
This document discusses strategies for making computer science more inclusive and addressing stereotypes. It summarizes views from several experts and provides recommendations such as splitting introductory courses based on experience to better serve beginners, recognizing that learning takes time and effort, learning about and addressing unconscious bias, building community among students, and making computer science a required subject. The overall message is that small changes can help broaden participation and make the field more welcoming to those from different backgrounds.
Implicit bias in higher ed - for undergraduatesKim Cobb
A brief overview of the concept of implicit bias as it relates to a campus setting, specifically designed for an undergraduate audience. Discussion-oriented slide set.
Ginny Catania presented on improving belonging in Greenland science. She noted that the cryospheric sciences are dominated by men, with the AGU Cryosphere Section having 3 times more male than female members. Some reasons for this disparity include unconscious and conscious bias, a chilly academic climate, and unequal access to resources such as social support networks and funding opportunities. Examples were provided of ways in which bias manifests, such as overburden of service work, lack of mentorship and collaboration, and harassment. The presentation discussed how the university system and field of glaciology were historically designed to exclude women, and how notions of academic skill still emphasize outdoor experiences more common among men.
This document discusses improving belonging and representation in Greenland science. It summarizes that women and other minoritized groups are underrepresented in cryospheric sciences. Specifically, the AGU Cryosphere Sciences Section has 3 times more men than women. It explores reasons for this disparity, such as unconscious and conscious bias, a chilly academic climate, and unequal access to resources. The document outlines specific examples of how bias shows up, such as overburden of service work or lack of mentorship. It proposes that the research culture needs to change and discusses the impacts of academic culture on diversity. Finally, it summarizes the successes and challenges of a new Slack workspace community aimed at supporting underrepresented groups in glaciology and
The Deficit Narrative of College Men: How Would Cardinal John Henry Newman Re...Daniel Zepp
The Deficit Narrative of College Men: How Would Cardinal John Henry Newman Respond? The Association for Student Affairs at Catholic Colleges and Universities (ASACCU) Conference, July 2012, University of Notre Dame, South Bend, IN.
The document discusses research being conducted on women in STEM disciplines. It summarizes three projects: 1) Examining how applicable pipeline and climate metaphors are to women's actual experiences in STEM fields and identifying new metaphors. 2) Using institutional ethnography to understand how women faculty experience their institution through policies and identifying disconnects between intent and experience. 3) Using personal narratives to understand how underrepresented students describe interacting with educational institutions and revealing institutional factors that affect their persistence. The goal is to help engineering education researchers better understand gender through theoretical frameworks and diverse methodologies.
Millennials can smell marketing-speak a mile away. So why does higher ed still write things like this?
"[Insert school here] delivers an exemplary learning experience that engages the best and brightest people, challenging them to meet ever-higher standards in the classroom and beyond."
"Show, Don't Tell" is a communication tactic that presents sensory details and substantive facts and lets people come to their own conclusions. It's easy to tell people what you want them to think, but when you give them the freedom to reach their own conclusions, they'll believe them.
In "Secrets of Show Don't Tell," David Poteet (President, NewCity) outlines the essentials of this communication tool and shows you colleges and universities that are doing it well.
Updated: My experience with tackling ongoing barriers faced by Women in STEM ...Dawn Bazely
Dawn Bazely discusses her experience advocating for women in STEM fields over several decades. She notes that while policies in the 1970s-90s aimed to increase the number of women in STEM, they failed to shift cultural norms due to unconscious biases. Recent research on implicit biases and social media movements like #MeToo have led to a greater awareness of barriers like harassment. Bazely emphasizes the importance of addressing retention, not just the pipeline, and highlights the role of social media in connecting advocates and applying pressure for policy changes to promote diversity and inclusion.
My experience with tackling ongoing barriers faced by Women in STEM in CanadaDawn Bazely
Talk for Women Studies, Visva Bharati University, Santiniketan, West Bengal, India. March 17, 2018.
I will update this to reflect the nasty article published by Science Magazine (AAAS) containing an attack on a young woman who is a PhD student and who also does a lot of innovative science outreach and engagement.
Overview of "The Science of Gender and Science" - the Pinker/Spelke Debate, b...Amy Goodloe
Steven Pinker and Elizabeth Spelke, both Harvard psychology professors, debated whether innate differences or discrimination explain the lack of women in science careers. Pinker argued innate gender differences in interests, variability in abilities, and spatial skills favor men in math and science. However, Spelke countered that studies show no inherent differences in children and biases influence perceptions of gender abilities. She provided multiple examples showing social and parental expectations, not innate factors, impact career choices. While both made arguments, Spelke supported her position that discrimination, not biology, creates disparities with extensive evidence from research in her field of expertise.
The Science of Gender and Science: Pinker vs. SpelkeAmy Goodloe
Steven Pinker and Elizabeth Spelke, both Harvard psychology professors, debated whether innate differences or discrimination explain the lack of women in science careers. Pinker argued innate gender differences in interests, variability in abilities, and spatial skills favor men in math and science. Spelke countered that gender differences only appear on subjective tests and are due to parents and teachers having lower expectations of girls, not innate factors. She cited studies finding infants and young children develop skills like those needed in science equally, regardless of gender. While both made arguments, Spelke supported her view with multiple examples from literature in her area of expertise.
PPT - A Look At How To Get Into Princeton University PowerPoint ...Joy Smith
This document outlines the steps to request writing assistance from HelpWriting.net:
1. Create an account with a password and valid email.
2. Complete a 10-minute order form providing instructions, sources, deadline, and attaching a sample for style imitation.
3. Review bids from writers for qualifications, history, and feedback, then deposit funds to start the assignment.
4. Ensure the paper meets expectations and authorize final payment, or request free revisions. Multiple revisions are allowed to ensure satisfaction. HelpWriting.net guarantees original, high-quality content or a full refund.
How To Write My College Essay. Online assignment writing service.Karrie Garcia
This document provides instructions for writing a college essay by outlining a 5-step process:
1. Create an account and provide contact information.
2. Complete a form with essay details, sources, and deadline to request assistance.
3. Review bids from writers and choose one based on qualifications.
4. Review the completed essay and authorize payment if satisfied.
5. Request revisions as needed, with guarantees of originality and refunds for plagiarism.
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20240605 QFM017 Machine Intelligence Reading List May 2024
The Computer Scientist and the Cleaner v4
1. The Computer
Scientist and the
Cleaner
Ian Gent
University of St Andrews
This is a DRAFT talk, version 4
For more context please visit:
http://iangent.blogspot.co.uk/2013/10/the-computer-scientist-and-cleaner.html
Wednesday, 9 October 13
8. What is this talk, really?
A short talk about gender balance and equality in
computer science.
Wednesday, 9 October 13
9. What is this talk, really?
Not an examinable part of CS1002 in a formal sense,
but something I think you should be exposed to
Wednesday, 9 October 13
10. The Computer Scientist
and the Cleaner
• Let me tell you a story.
“The computer scientist and the cleaner had
a long and happy marriage. One of their few
arguments was when she forgot their
wedding anniversary. But their marriage was
strong and he forgave her.”
Wednesday, 9 October 13
11. The Computer Scientist
and the Cleaner
“One of their few arguments was when she
forgot their wedding anniversary.”
• Let me ask you a question.
• Who forgot the anniversary?
• Was it the computer scientist or the
cleaner?
Wednesday, 9 October 13
12. The Computer Scientist
and the Cleaner
“One of their few arguments was when she
forgot their wedding anniversary.”
• Let me ask Google a question.
• Who forgot the anniversary?
• Was it the computer scientist or the
cleaner?
Wednesday, 9 October 13
15. Who forgot the
anniversary?
“The computer scientist and the cleaner had
a long and happy marriage. One of their few
arguments was when she forgot their
wedding anniversary. But their marriage was
strong and he forgave her.”
• Look inside your brain
• Did you think the woman was the
cleaner?
Wednesday, 9 October 13
16. A short history of
sexism in St Andrews
• The University is 600 years old, yet ...
• Its first female professor was a computer
scientist!
• Prof Ursula Martin CBE, now at QMUL
• Yes, a 600 year old University’s first
female professor hasn’t retired yet!
• For 579 years we didn’t have a female
Prof
• Until 2004, the Rules of Golf for women
were made by a male only club in St
Andrews
• For 595 years we didn’t have a female
Principal
• Prof Louise Richardson
Wednesday, 9 October 13
22. Let’s be clear...
• The University of St Andrews does not have
sexist hiring policies
• We have clear non-sexist hiring policies
• http://www.st-andrews.ac.uk/hr/edi/inclusiverec/
• The gender balance in St Andrews CS
• reflects general imbalance in the discipline
• and it’s a big problem
Wednesday, 9 October 13
24. Why the most important
problem?
Why should we have more women in CS?
I only know of two good reasons, but they
are overpoweringly good.
• It’s right
• Computer Science would be better
Wednesday, 9 October 13
25. It’s Right
• If a woman doesn’t want to do CS, that’s fine
• But ...
• CS is an incredibly rewarding discipline
• If a woman is put off CS they are potentially
missing out
• That is NOT fine
• Everybody in CS is responsible for making
sure this doesn’t happen
Wednesday, 9 October 13
26. Computer Science
Would Be Better
• “Computing's too important to be left to
men”
Karen Spärck Jones, 1935-2007
• Karen did a bit more than a cute quote
• She invented a key technique for internet
search ...
• ... 30 years before the World Wide Web
• Don’t throw away half the world’s talents!
Karen Spärck Jones, imageWikipedia
Wednesday, 9 October 13
27. But is it a problem now?
• In the past this was a problem
• And it still is
• An almost random example
• Science faculty’s subtle gender biases favor
male students
• Proceedings National Academy of
Science, USA, 2012
• http://www.pnas.org/content/early/
2012/09/14/1211286109
• The change from the past is that gender
biases are now subtle
In addition to determining whether faculty expressed a bias
against female students, we also sought to identify the processes
contributing to this bias. To do so, we investigated whether
faculty members’ perceptions of student competence would help
to explain why they would be less likely to hire a female (relative
to an identical male) student for a laboratory manager position.
Additionally, we examined the role of faculty members’ preex-
isting subtle bias against women. We reasoned that pervasive
cultural messages regarding women’s lack of competence in sci-
ence could lead faculty members to hold gender-biased attitudes
that might subtly affect their support for female (but not male)
science students. These generalized, subtly biased attitudes to-
ward women could impel faculty to judge equivalent students
differently as a function of their gender.
The present study sought to test for differences in faculty
perceptions and treatment of equally qualified men and women
pursuing careers in science and, if such a bias were discovered,
reveal its mechanisms and consequences within academic sci-
ence. We focused on hiring for a laboratory manager position as
the primary dependent variable of interest because it functions as
a professional launching pad for subsequent opportunities. As
secondary measures, which are related to hiring, we assessed: (i)
perceived student competence; (ii) salary offers, which reflect
the extent to which a student is valued for these competitive
positions; and (iii) the extent to which the student was viewed as
deserving of faculty mentoring.
Our hypotheses were that: Science faculty’s perceptions and
treatment of students would reveal a gender bias favoring male
students in perceptions of competence and hireability, salary
conferral, and willingness to mentor (hypothesis A); Faculty gen-
der would not influence this gender bias (hypothesis B); Hiring
These results support hypothesis A.
In support of hypothesis B, faculty gender did not affect bias
(Table 1). Tests of simple effects (all d < 0.33) indicated that
female faculty participants did not rate the female student as
more competent [t(62) = 0.06, P = 0.95] or hireable [t(62) = 0.41,
P = 0.69] than did male faculty. Female faculty also did not
offer more mentoring [t(62) = 0.29, P = 0.77] or a higher salary
[t(61) = 1.14, P = 0.26] to the female student than did their male
Fig. 1. Competence, hireability, and mentoring by student gender condition
(collapsed across faculty gender). All student gender differences are significant
(P < 0.001). Scales range from 1 to 7, with higher numbers reflecting a greater
extent of each variable. Error bars represent SEs. nmale student condition = 63,
nfemale student condition = 64.
2 of 6 | www.pnas.org/cgi/doi/10.1073/pnas.1211286109 Moss-Racusin et al.
• Male students were
ranked higher in
everything
• The only difference
between the male and
female students was the
names on CVs
Wednesday, 9 October 13
28. The lowest difficulty
setting there is
John Scalzi, http://whatever.scalzi.com/2012/05/15/straight-white-
male-the-lowest-difficulty-setting-there-is/
• It’s really hard for straight white men to
understand the problem
• sadly it’s really easy for women to
• John Scalzi came up with a brilliant analogy
• Being a straight white male is the lowest
difficulty setting there is in the game of life
“You can lose playing on the lowest difficulty
setting.The lowest difficulty setting is still the
easiest setting to win on.The player who
plays on the “Gay Minority Female” setting?
Hardcore.”
Wednesday, 9 October 13
29. What can we do?
• We can’t change today the gender
imbalance
• We can make CS a much nicer place for
women to be
• We can do three simple things...
Wednesday, 9 October 13
30. Three Simple Things
1. Don’t be a jerk to women in CS
2. Don’t use sexist language
3. Understand that it’s not you who decides
if you are doing 1 or 2.
Wednesday, 9 October 13
31. Not just women
1. Don’t be a jerk to disabled in CS
2. Don’t use ableist language
3. Understand that it’s not you who decides
if you are doing 1 or 2.
Wednesday, 9 October 13
32. Not just women
1. Don’t be a jerk to non-whites in CS
2. Don’t use racist language
3. Understand that it’s not you who decides
if you are doing 1 or 2.
Wednesday, 9 October 13
33. Not just women
1. Don’t be a jerk to people from deprived
backgrounds in CS
2. Don’t use classist language
3. Understand that it’s not you who decides
if you are doing 1 or 2.
Wednesday, 9 October 13
34. Not just women
1. Don’t be a jerk to mentally ill people in
CS
2. Don’t use mentalist language
3. Understand that it’s not you who decides
if you are doing 1 or 2.
Wednesday, 9 October 13
35. Not just women
1. Don’t be a jerk to transgendered people
in CS
2. Don’t use cissexist language
3. Understand that it’s not you who decides
if you are doing 1 or 2.
Wednesday, 9 October 13
36. Not just women
1. Don’t be a jerk to older people in CS
2. Don’t use ageist language
3. Understand that it’s not you who decides
if you are doing 1 or 2.
Wednesday, 9 October 13
37. Not just women
1. Don’t be a jerk to gay people in CS
2. Don’t use homophobic language
3. Understand that it’s not you who decides
if you are doing 1 or 2.
Wednesday, 9 October 13
38. Not just women
1. Don’t be a jerk to religious people in CS
2. Don’t use religionist language
3. Understand that it’s not you who decides
if you are doing 1 or 2.
Wednesday, 9 October 13
39. Not just women
1. Don’t be a jerk to irreligious people in
CS
2. Don’t use religionist language
3. Understand that it’s not you who decides
if you are doing 1 or 2.
Wednesday, 9 October 13
40. None of the above?
• Even if it was ok to be a jerk to ...
• Straight white privileged non-disabled non-mentally-ill
cisgendered male of about your age and your religion
• (it’s not ok to be a jerk to them)
• How do you know they’re all those things?
• e.g. I’m obviously older than you...
• is it so obvious I’m on antidepressants?
• http://www.depressedacademics.blogspot.com
Wednesday, 9 October 13
41. Back to Women
• Going to return to focus on women
• Not because other groups are not
important
• Just to make it easier to talk specifically
Wednesday, 9 October 13
42. 1. Don’t be a jerk
• This is really simple to understand
• Unfortunately being a jerk to women in CS is
really widespread
• I’m not going to provide examples
• it would take too long
• seriously, it’s almost unimaginable how long it
would take
Wednesday, 9 October 13
43. 2. Don’t use sexist
language
• I mean this in two ways
• Don’t use language that implies CS people are men
• leads to the Computer Scientist and the
Cleaner
• leads to females feeling excluded
• and subtle biases as in the PNAS paper
• Don’t engage in sexist “banter”
Wednesday, 9 October 13
44. 3.You don’t get to
decide...
3. Understand that it’s not you who decides if you are doing 1 or 2.
• This is really hard to understand
• Maybe you think somebody shouldn’t be offended when they tell
you they are
• Tough! Guess what, they were offended!
• You only have two options
• “I’m sorry, but I deeply believe that X is true so I stand by
what I said”
• “I’m sorry, I’ll try harder not to say things like that in future”
• Never say “Hey, it’s only banter”
Wednesday, 9 October 13
45. It’s not “banter”
• "Banter" is apparently a free pass: I can insult you, but you're not
allowed to be insulted, because "it's only banter". I can be
obscene, but you can't be offended, because "it's only banter".
No. If you're a grown-up, you know that your offensiveness may
offend, and you either accept that or you apologise and don't do
it again. Saying "it's only banter" makes you not only an idiot, but
an idiot who can't take responsibility for his own jokes.
Tom Chivers,
http://blogs.telegraph.co.uk/news/tomchiversscience/100141906/
if-you-like-banter-you-are-an-idiot/
Wednesday, 9 October 13
46. The Hofstadter Analogy
• If you’re not sure if language is sexist...
• ... swap men and women for black and white
• If the result is obviously racist
• ... the original was probably sexist ...
• unless there’s some very good reason the analogy doesn’t work
• E.g. A male only club made the rules of golf, including for women
• Would it have been ok that a white only club made the rules of
golf, including the rules for black people?
• I learnt this from:
• “A person paper on the purity of language”, Doug Hofstadter
• http://www.cs.virginia.edu/~evans/cs655/readings/purity.html
Wednesday, 9 October 13
47. Allies
• We need male computer scientists to be
“Allies”
• Men who think it’s important that both
women and men are treated right in
Computer Science
• http://geekfeminism.wikia.com/wiki/Allies
Wednesday, 9 October 13
48. Links and Resources
• School of Computer Science, Women in Computing Group
• self-organised, unofficial
• University Policies and Links
• Advice and Support Centre
• University Harrassment and Bullying Policy
• Student Non-academic Misconduct Policy
• Disability Equality Scheme
• Policy on Trans Students & Staff
• How to Lodge a Complaint
Wednesday, 9 October 13
49. Do not get me wrong...
Whether you are disadvantaged or privileged or both...
I want you to have a fabulous time at St Andrews
I want you to get a first
I want you to have an amazing career in or out of computing
I want the same for every member of groups at a disadvantage
And I want us all to work towards them not being at a disadvantage
Wednesday, 9 October 13