This document provides guidance for evaluating web sources for research by beginning MBA students. It outlines criteria for evaluating sources, including authority, accuracy, content, currency, and objectivity. For each criterion, it poses questions to consider and provides techniques for evaluating the source based on that criterion, such as looking for information about authors and publishers to judge authority, consulting additional sources to verify claims and check for errors when evaluating accuracy, and examining links and references for signs of bias or an agenda when analyzing objectivity. The document encourages using these criteria and techniques to make sense of excessive online information and develop critical thinking skills.
https://doi.org/10.6084/m9.figshare.11854626.v1
Presented at Dutch National Librarian/Information Professianal Association annual conference 2011 - NVB2011
November 17, 2011
Developed by Mike Eisenberg, Professor of Information Science at Syracuse University and Bob Berkowitz, a practicing library media professional in Syracuse, New York in the year 2000.
The Big 6 incorporates information search and use in a systematic process to find, use, apply, and evaluate information for specific needs.
An overview of FRBR focused on the WEMI model, User Tasks, and cataloging as service. Also touches on the new Library Reference Model, LRM and its impact on FRBR.
https://doi.org/10.6084/m9.figshare.11854626.v1
Presented at Dutch National Librarian/Information Professianal Association annual conference 2011 - NVB2011
November 17, 2011
Developed by Mike Eisenberg, Professor of Information Science at Syracuse University and Bob Berkowitz, a practicing library media professional in Syracuse, New York in the year 2000.
The Big 6 incorporates information search and use in a systematic process to find, use, apply, and evaluate information for specific needs.
An overview of FRBR focused on the WEMI model, User Tasks, and cataloging as service. Also touches on the new Library Reference Model, LRM and its impact on FRBR.
Balancing the Dimensions of User IntentTrey Grainger
The first step in returning relevant search results is successfully interpreting the user’s intent. This requires combining a holistic understanding of your content, your users, and your domain. Traditional keyword search focuses on the content understanding dimension. Knowledge graphs are then typically built and leveraged to represent an understanding of your domain. Finally, Collaborative recommendations and user profile learning are typically the tools of choice for generating and modeling an understanding of the preferences of each user.
While these systems (search, recommendations, and knowledge graphs) are often built and used in isolation, combining them together is the key to truly understanding a user’s query intent. For example, combining traditional keyword search with your knowledge graph leads to semantic search capabilities, and combining traditional keyword search with recommendations leads to personalized search experiences. Combining all of these dimensions together in an appropriately balanced way will ultimately lead to the most accurate interpretation of a user’s query, resulting in a better query to the core search engine and ultimately a better, more relevant search experience.
In this talk, we’ll demonstrate strategies for delivering and combining each of these dimensions of user intent, and we’ll walk through concrete examples of how to balance the nuances of each so that you also don’t over-personalize, over-contextualize, or under appreciate the nuances of your user’s intent.
My presentation at the Semantic Technology and Business Conference in San Jose on August 19, 2014, with Barbara Starr (Her slides are separate, and cover a vast array of semantic tools and approaches for assessing and understanding your pages).
In this file, you can ref interview materials for library such as, library situational interview, library behavioral interview, library phone interview, library interview thank you letter, library interview tips …
Lexical Semantics, Semantic Similarity and Relevance for SEOKoray Tugberk GUBUR
Lexical semantics and relations between words include relations of superiority, inferiority, part, whole, opposition, and sameness between the meanings of words. The same word can be a meronymy, hyponym, or antonym of another word, depending on the word before or after it. The lexical relation value of the first word can affect the structure of the next word, affecting the context of the sentence and the Information Retrieval Score. Information Retrieval Score is the score that determines how much content is related to a query, how close the different variants of the related query are, and the structure processed by the search engine’s query processor to the relevant document. A higher information retrieval score represents better relevance and possible click satisfaction.
The problem with a semi-structured and distracting context for Information Retrieval Score is that, if a document is not configured for a single topic, the IR Score can be diluted by the two different contexts resulting in a relative rank lost to another textual document.
IR Score Dilution involves badly structured lexical relations, along with bad word proximity. The relevant words that complete each other within the meaning map should be used closely, within a paragraph or section of the document, to signal the context in a more clear way to increase the IR Score. A search engine can check whether the document contains the hyponym of the words within the query or not. A possible query prediction can be generated from the hypernyms of the query. A search engine can check only the anchor texts to see whether there is a word within the “hyponym distance” which represents the hyponym depth between two different words.
Lexical Relations can represent the semantic annotations for a document. A semantic annotation is a word that describes the document overall in terms of category and main context that carries the purpose of the document. A semantic annotation can contain the main entity of the document or a general concept for covering a broader meaning area (knowledge domain). Semantic Annotations can be generated with the lexical relations between words. A semantic annotation can be used to match the document to the query. Semantic annotations are factors for a better IR Score.
A search engine can generate phrase patterns from the lexical relationships between words within the queries or the documents. A phrase pattern contains sections that define a concept with qualifiers. Phrase patterns can contain a hyponym just after an adjective, or a hypernym with the antonym of the same adjective. Most of these connections and patterns are used within the Recurrent Neural Network (RNN) for the next word prediction. A phrase pattern helps a search engine to increase its confidence score for relating the document to the specific query, or the meaning of the query.
Thought Vectors and Knowledge Graphs in AI-powered SearchTrey Grainger
While traditional keyword search is still useful, pure text-based keyword matching is quickly becoming obsolete; today, it is a necessary but not sufficient tool for delivering relevant results and intelligent search experiences.
In this talk, we'll cover some of the emerging trends in AI-powered search, including the use of thought vectors (multi-level vector embeddings) and semantic knowledge graphs to contextually interpret and conceptualize queries. We'll walk through some live query interpretation demos to demonstrate the power that can be delivered through these semantic search techniques leveraging auto-generated knowledge graphs learned from your content and user interactions.
The concept of information seeking behavior by using Wilsons’ (1996) revised ...Lucy Kasuke
Information seeking behavior resulted from the recognition of some needs, perceived by the user, who as a consequence makes demand upon on formal systems such as libraries and information centers, or some other person in order to satisfy the perceived information need, thus information seeking is a human process with adaptive and reflective control over the afferent and efferent actions of the information seeker (Kundu, 2017).
Balancing the Dimensions of User IntentTrey Grainger
The first step in returning relevant search results is successfully interpreting the user’s intent. This requires combining a holistic understanding of your content, your users, and your domain. Traditional keyword search focuses on the content understanding dimension. Knowledge graphs are then typically built and leveraged to represent an understanding of your domain. Finally, Collaborative recommendations and user profile learning are typically the tools of choice for generating and modeling an understanding of the preferences of each user.
While these systems (search, recommendations, and knowledge graphs) are often built and used in isolation, combining them together is the key to truly understanding a user’s query intent. For example, combining traditional keyword search with your knowledge graph leads to semantic search capabilities, and combining traditional keyword search with recommendations leads to personalized search experiences. Combining all of these dimensions together in an appropriately balanced way will ultimately lead to the most accurate interpretation of a user’s query, resulting in a better query to the core search engine and ultimately a better, more relevant search experience.
In this talk, we’ll demonstrate strategies for delivering and combining each of these dimensions of user intent, and we’ll walk through concrete examples of how to balance the nuances of each so that you also don’t over-personalize, over-contextualize, or under appreciate the nuances of your user’s intent.
My presentation at the Semantic Technology and Business Conference in San Jose on August 19, 2014, with Barbara Starr (Her slides are separate, and cover a vast array of semantic tools and approaches for assessing and understanding your pages).
In this file, you can ref interview materials for library such as, library situational interview, library behavioral interview, library phone interview, library interview thank you letter, library interview tips …
Lexical Semantics, Semantic Similarity and Relevance for SEOKoray Tugberk GUBUR
Lexical semantics and relations between words include relations of superiority, inferiority, part, whole, opposition, and sameness between the meanings of words. The same word can be a meronymy, hyponym, or antonym of another word, depending on the word before or after it. The lexical relation value of the first word can affect the structure of the next word, affecting the context of the sentence and the Information Retrieval Score. Information Retrieval Score is the score that determines how much content is related to a query, how close the different variants of the related query are, and the structure processed by the search engine’s query processor to the relevant document. A higher information retrieval score represents better relevance and possible click satisfaction.
The problem with a semi-structured and distracting context for Information Retrieval Score is that, if a document is not configured for a single topic, the IR Score can be diluted by the two different contexts resulting in a relative rank lost to another textual document.
IR Score Dilution involves badly structured lexical relations, along with bad word proximity. The relevant words that complete each other within the meaning map should be used closely, within a paragraph or section of the document, to signal the context in a more clear way to increase the IR Score. A search engine can check whether the document contains the hyponym of the words within the query or not. A possible query prediction can be generated from the hypernyms of the query. A search engine can check only the anchor texts to see whether there is a word within the “hyponym distance” which represents the hyponym depth between two different words.
Lexical Relations can represent the semantic annotations for a document. A semantic annotation is a word that describes the document overall in terms of category and main context that carries the purpose of the document. A semantic annotation can contain the main entity of the document or a general concept for covering a broader meaning area (knowledge domain). Semantic Annotations can be generated with the lexical relations between words. A semantic annotation can be used to match the document to the query. Semantic annotations are factors for a better IR Score.
A search engine can generate phrase patterns from the lexical relationships between words within the queries or the documents. A phrase pattern contains sections that define a concept with qualifiers. Phrase patterns can contain a hyponym just after an adjective, or a hypernym with the antonym of the same adjective. Most of these connections and patterns are used within the Recurrent Neural Network (RNN) for the next word prediction. A phrase pattern helps a search engine to increase its confidence score for relating the document to the specific query, or the meaning of the query.
Thought Vectors and Knowledge Graphs in AI-powered SearchTrey Grainger
While traditional keyword search is still useful, pure text-based keyword matching is quickly becoming obsolete; today, it is a necessary but not sufficient tool for delivering relevant results and intelligent search experiences.
In this talk, we'll cover some of the emerging trends in AI-powered search, including the use of thought vectors (multi-level vector embeddings) and semantic knowledge graphs to contextually interpret and conceptualize queries. We'll walk through some live query interpretation demos to demonstrate the power that can be delivered through these semantic search techniques leveraging auto-generated knowledge graphs learned from your content and user interactions.
The concept of information seeking behavior by using Wilsons’ (1996) revised ...Lucy Kasuke
Information seeking behavior resulted from the recognition of some needs, perceived by the user, who as a consequence makes demand upon on formal systems such as libraries and information centers, or some other person in order to satisfy the perceived information need, thus information seeking is a human process with adaptive and reflective control over the afferent and efferent actions of the information seeker (Kundu, 2017).
Used to teach evaluating sources, particularly web sources, to my high school students.
Adapted from Cathy Nelson's Information Evaluation slideshare: http://www.slideshare.net/cnelson/web-evaluation-2015135
Ethical Considerations in Technical Writing and the WorkplaceThe Integral Worm
Description
This presentation outlines ethical issues in technical writing and the workplace. The subject matter covered is as follows:
* The levels of response to persuasion
* Three strategies for connectivity with an audience
* Every writing situation poses its own constraints
* Workplace pressures can influence ethical values
* Groupthink can be a handy hiding place
* Some legal lies in the workplace
* Reasonable criteria for ethical judgement
* An ethical checklist for communicators
How to Write Technical Writing , purpose to write technically ,things to avoid about technical writing , target audience about TW
Please visit my blog for more information
http://studyofcs.blogspot.com/search/label/Education
This presentation discussed Kathy Schrock's "5 W's" construct and how to use it to assess the validity or web content. This is a companion piece to the article published on EmergingEdTech.com [URL]
This is a minute assumed as 2 presiding officer. (dont know exactly what we should call it *presiding* coz there is only 1 presiding officer iin a meeting)
Please follow these steps1. Choose a topic from the subject lis.docxmattjtoni51554
Please follow these steps:
1. Choose a topic from the subject list
2. Research four (4) sites dealing with the same topic you have selected. (Do not use Wikipedia, About.com, or Google as final source sites)
3. Write a brief critical evaluation report on each of the four sites you have visited. (Please include hyperlink connections to the four sites.)
4. Your evaluation must have a title page listing the topic, your name and the name of the instructor, the course title and section number, and the date.
5. Your paper must be in Word format ( doc. or docx.) or a Rich Text Format (.rtf) format (if using a different word processing program). The total report should be type-written, double-spaced, and 600 - 800 words in length. Papers are expected to demonstrate quality collegiate writing.
Submit your paper in the Web Evalution Drop Box. Check the When Assignments are Due page for due date.
Web Site Evaluation Criteria
· Authority--
· Does the resource have some reputable organization or expert behind it?
· Does the author have standing in the field? How do you know?
· Content
· What aspects of the subject are covered (breadth)?
· What is the level of detail provided about the subject (depth)?
· Is the information fact or opinion?
· Does the site contain original information or simply links?
· Accuracy
· Is the information in the resource accurate?
· How do you know?
· Currency
· Is the resource updated or static?
· Objectivity
· How biased is the site?
· Does it carry balanced information based on objective research or does it convey propaganda and subjective opinions?
Suggested Topics for WEB Project
1. Climate Change
2. Space Exploration
3. Biotechnology / Medical Innovations
4. Nanotechnology
5. Communication Technologies / Social Media
6. Alternative Energy Sources
7. Artificial Intelligence / Robotics
8. Green Jobs of the (not so distant)Future
9. Have you seen it?? (Latest Innovations)
10. Women Inventors & Scientists
11. Reuse,Repurpose,Recycle
12. Security,Surveillence, and Drones
Is the Web a good research tool? This question is dependent on the researcher's objective. As in traditional print resources, one must use a method of critical analysis to determine its value. Here is a checklist for evaluating web resources to help in that determination.
Authority:
Is the information reliable?
Check the author's credentials and affiliation. Is the author an expert in the field?
Does the resource have a reputable organization or expert behind it?
Are the sources of information stated? Can you verify the information?
Can the author be contacted for clarification?
Check for organizational or author biases.
Scope:
Is the material at this site useful, unique, accurate or is it derivative, repetitious, or doubtful?
Is the information available in other formats?
Is the purpose of the resource clearly stated? Does it fulfill its purpose?
What items are included in the resource? What subject area, time period, formats or types of material .
Knowing What’s Best: Discover How the Portal Does Your Homework Health Evidence™
Slides from a 60 minute webinar on learning how to use the Web Resource Ratings feature of the McMaster Optimal Aging Portal www.mcmasteroptimalaging.org/ to sort through the masses of health-related information on the internet in order to identify only the most high-quality, trustworthy and reliable information about optimal aging. This work was funded by the Labarge Optimal Aging Initiative.
Maureen Dobbins, McMaster professor, lead the webinar on October 21, 2014.
Always be aware of the information you find. Who wrote it? Is there bias? Is it current? Is it correct? The librarians from the Meriam Library at CSU Chico created this test to evaluate information.
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Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
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In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
The French Revolution Class 9 Study Material pdf free download
Evaluating Web Sources For Research
1. Evaluating Web Sources for Research Instruction for beginning MBA students By Mahrya Carncross, Librarian Candidate, City University of Seattle Library
2. Information Literacy: Skills for Research Success Association of College and Research Libraries (2003). Information Literacy Standards for Higher Education. Retrieved February 23, 2009 at http://www.ala.org/ala/mgrps/divs/acrl/standards/informationliteracycompetency.cfm .
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15. The Library is Here to Help! Library > Resources by Program > Management
