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University of Nottingham WinSET Debate

University of Nottingham WinSET Debate. 3 July 2013. So called, scissors diagrams are often used to illustrate the “leaky pipeline” for women along a career pathway. HE career progression at UK HE institutions by gender, Maths 2007/08. Data source: HESA (2008).

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University of Nottingham WinSET Debate

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  1. University of NottinghamWinSET Debate 3 July 2013

  2. So called, scissors diagrams are often used to illustrate the “leaky pipeline” for women along a career pathway

  3. HE career progression at UK HE institutions by gender, Maths 2007/08 Data source: HESA (2008)

  4. Pipeline: Progression in physics by gender, 2007/08 Data source: HESA (2008)

  5. Two key issues are recruitment into a subject at UG level and subsequent retention of those students into the profession • A crude measure of the leakage of women along the higher education pipeline is the ratio of the proportion of UGs who are female and the proportion of professors who are female

  6. Pipeline: Progression in physics by gender, 2007/08 [UG(%F):P(%F) ~ 4:1] Data source: HESA (2008)

  7. Pipeline: progression in biology by gender 2007/08 [UG(%F):P(%F) ~ 3.5:1] Data source: HESA (2008)

  8. HE career progression at UK HE institutions by gender, Maths 2007/08 [UG(%F):P(%F) ~ 9:1] Data source: HESA (2008)

  9. Pipeline: progression in chemistry by gender, 2007/08 [UG(%F):P(%F) ~ 7.5:1] Data source: HESA (2008)

  10. Pipeline: progression in clinical medicine by gender 2010/11 [UG(%F):P(%F) ~ 2.5:1] Chemistry data used for GCSE and A Level Data source: HESA (2008)

  11. Pipeline: progression in psychology by gender, 2011/12 [UG(%F):P(%F) ~ 2.5:1] Data source: HESA (2008)

  12. Perhaps the so-called “leaky pipeline” is just a reflection of times when smaller proportion of women read science • Perhaps women just choose to leave academia

  13. Examining the proportions of male and female permanent academic staff who are professors, respectively, in particular age bands provides a comparison of the likelihood of men and women being professors, irrespective of the make up or history of the cohort

  14. Proportion permanent academic staff who are professors by age (all cost centres) Data source: HESA (2012)

  15. Proportion permanent academic staff who are professors by age (physics) Data source: HESA (2012)

  16. Proportion permanent academic staff who are professors by age (chemistry) Data source: HESA (2012)

  17. Proportion permanent academic staff who are professors by age (mathematics) Data source: HESA (2010)

  18. Nationally, a significant proportion of mathematics staff are classified as “teaching-only”. Women are more likely to be in teaching-only positions than men. Examining the mathematics staff data excluding teaching-only staff give a different picture, albeit women are still less likely than men to be professors

  19. Proportion permanent academic staff who are professors by age (mathematics: without teaching-only staff) Data source: HESA (2012)

  20. Proportion permanent academic staff who are professors by age (biosciences) Data source: HESA (2012)

  21. Proportion permanent academic staff who are professors by age (psychology) Data source: HESA (2012)

  22. Proportion permanent academic staff who are professors by age (Nursing & Paramedical Studies) Data source: HESA (2012)

  23. In only one cost centre women more likely to be professors than men, chemical engineering

  24. Proportion permanent academic staff who are professors by age (chemical engineering) Data source: HESA (2012)

  25. In every subject bar one, men are more likely to be professors than women, often significantly so • The biggest disparities are in materials and mathematics • If you are male and want to be a professor, study physics • If you are female and want to be a professor, study chemical engineering

  26. Thank you Sean McWhinnie Tel: 01235 439188 Email: sean.mcwhinnie@oxfordresearchandpolicy.co.uk

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