Mind the gap: gender disparities still to be addressed in <scp>UK</scp> Higher Education geography
Bibliographic record
Abstract
This paper evidences persistent gender inequalities in UK higher education ( HE ) geography departments. The two key sources of data used are: Higher Education Statistics Agency ( HESA ) data for staff and students, which affords a longitudinal response to earlier surveys by M c D owell and M c D owell and Peake of women in UK university geography departments, and a qualitative survey of the UK HE geography community undertaken in 2010 that sought more roundly to capture respondent reflections on their careers, choices, status and experiences. Findings show that although the gender gap is closing within HE geography in the UK there are significant ongoing gender disparities. Therefore, the paper argues that the long and demanding process of reducing gender inequalities (alongside other, equally vital intersectional inequalities) requires continued commitment. Furthermore, respondents evidence the cost of these inequalities: enablers and barriers to job security and career progression can have long‐term impacts on quality of life and financial security, and affect personal life decisions. In recent years the UK ‐based A thena S wan and Gender Equality Charter Mark agendas have prompted universities to address gendered disparities and the authors note a changing zeitgeist. The survey findings point to the need for sustained leadership within geography departments to address the day‐to‐day gender – and other – inequalities experienced in the workplace.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".