Staff disability data in UK higher education: Evidence from EDI reports
Bibliographic record
Abstract
OBJECTIVES: To explore how higher education institutions (HEIs) make transparent the data they collect on staff disability, and how this relates to existing equality, diversity and inclusion (EDI) charters. DESIGN: Descriptive cross-sector quantitative study based on UK HEIs. SETTING: Higher education sector in the UK. PARTICIPANTS: 162 HEIs across the UK with information extracted from the Higher Education Statistics Agency (HESA), each institution's website and Advance HE. PRIMARY AND SECONDARY OUTCOME MEASURES: Availability of a publicly available EDI report. Type of information on staff disability identified within the EDI report and level of detail, the latter derived from the number of different types of information provided in the report. Athena SWAN and Disability Confident award level for each HEI were used as a proxy for the sector's commitment to EDI. RESULTS: Under a quarter of HEIs do not have an open EDI report online. The majority of Athena SWAN award holders make their EDI reports publicly available, which is similar by Disability Confident status. Russell Group universities are more likely to have a publicly available report. Regionally, EDI report availability is lowest in London. The level of detail with regards to staff disability varies, with more than half of institutions providing 'little detail' and just under a third 'some detail'. Athena SWAN award holders and Disability Confident members are twice as likely to provide 'some detail' than those which do not hold an award. CONCLUSIONS: Challenges remain to obtain a clear picture of staff with disabilities within higher education. The lack of both uniformity and transparency in EDI reporting with respect to disability hinders the ability to quantify staff with disabilities within higher education, develop meaningful interventions and address inequities more widely.
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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 | 0.091 | 0.466 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.034 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".