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Beyond Numerical Representation: Gender Statistics Slide 1 Beyond Numerical Representation: Gender Statistics Slide 2 Beyond Numerical Representation: Gender Statistics Slide 3 Beyond Numerical Representation: Gender Statistics Slide 4 Beyond Numerical Representation: Gender Statistics Slide 5 Beyond Numerical Representation: Gender Statistics Slide 6 Beyond Numerical Representation: Gender Statistics Slide 7 Beyond Numerical Representation: Gender Statistics Slide 8 Beyond Numerical Representation: Gender Statistics Slide 9 Beyond Numerical Representation: Gender Statistics Slide 10 Beyond Numerical Representation: Gender Statistics Slide 11 Beyond Numerical Representation: Gender Statistics Slide 12 Beyond Numerical Representation: Gender Statistics Slide 13 Beyond Numerical Representation: Gender Statistics Slide 14 Beyond Numerical Representation: Gender Statistics Slide 15 Beyond Numerical Representation: Gender Statistics Slide 16 Beyond Numerical Representation: Gender Statistics Slide 17
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Beyond Numerical Representation: Gender Statistics

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Presentation held by Anne Laure Humbert (Oxford Brookes University), during the conference "Structural gender change at universities and research funding organizations", an event of H2020 project SUPERA. Madrid, 16/11/2018.

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Beyond Numerical Representation: Gender Statistics

  1. 1. BEYOND NUMERICAL REPRESENTATION: GENDER STATISTICS Dr Anne Laure Humbert SUPERA Meeting Madrid, 16 November 2018
  2. 2. GENDER AND LANGUAGE Sex vs gender? Four layers of ‘gender’? ▪ Biological sex Sex ▪ Socially constructed gender Gender (identity) ▪ Performativity Gender (expression) ▪ Heteronormativity Sexual orientation
  3. 3. WHAT’S IN A WORD? Saussure’s process of signification: Signified: concept represented Signifier: form adopted
  4. 4. WHAT’S IN A WORD? Signification as sense giving that is subject to variation: Denotation: e.g. pictograms Connotation: e.g. different understandings of gender
  5. 5. WHAT’S IN A WORD?
  6. 6. WHAT’S IN A WORD?
  7. 7. WHAT’S IN A WORD?
  8. 8. WHAT’S IN A WORD? To understand the process of signification, Jakobson’s (1960) model states that it needs to be put in relation to the context, the code and the channel. ▪Context: is gender treated differently in gender-related or gender-sensitive environments? ▪Code: do gender understandings rely on familiarity with gender theory? ▪Channel: does gender language matter?
  9. 9. WORDS AND NUMBERS
  10. 10. Sex disaggregated data? vs Gender statistics?
  11. 11. Sex disaggregated data? (denotation) vs Gender statistics? (connotation)
  12. 12. GENDER DIVERSITY
  13. 13. COUNTING HEADS 50%50%
  14. 14. COUNTING HEADS 25% 75% 63% 37%
  15. 15. INDICATORS GENDER DIVERSITY INDEX Demographic Gender Diversity Age Education Marital Status Care Responsibilities % Wo/men below/above average team age % Wo/men with/out doctorate % Wo/men with/out cohabitating significant other % Wo/men with/out current care responsibilities Functional Gender Diversity Contract Team tenure Team role % Wo/men with/out permanent position % Wo/men below/above average team tenure % Wo/men with/out senior role
  16. 16. GENDER STATISTICS FOR REAL EQUALITY AT UNIVERSITIES ▪Build better understandings of what we mean by gender ▪Engage gender experts with gender statistics ▪Expand our gender perspective(s) to include other categories of power relations
  17. 17. BEYOND NUMERICAL REPRESENTATION: GENDER STATISTICS Dr Anne Laure Humbert SUPERA Meeting Madrid, 16 November 2018
  • AnaMendoza272926

    Dec. 1, 2021

Presentation held by Anne Laure Humbert (Oxford Brookes University), during the conference "Structural gender change at universities and research funding organizations", an event of H2020 project SUPERA. Madrid, 16/11/2018.

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