International and comparative perspectives on diversity management: an overview
Bibliographic record
Abstract
[Extract] International literature on diversity and equality started to slowly emerge in the early 2000s with contributions based on country by country contributions on equal employment opportunity (Agocs, 2002) and comparative research (Jain et al., 2003 ; Agocs and Osborne, 2009) about equality legislations. These first contributions that attempted to stretch beyond North America underlined that there were a variety of models of employment equity legislations, some more focused on quotas and outcomes, such as in India and Malaysia, some more focused on process changes, goals and timetables, such as in Canada and the USA. They also underscored that when it came to outcomes, i.e., impact of equality legislation on the gaps between women and men, or between majority and minority groups, the situation was that of a ‘half-full, half-empty’ glass with still very much room for improvement for women, or other disadvantaged groups, whose definitions varied from country to country (Catholics in Northern Ireland, Bumiputras in Malaysia, Dalits in India, etc..).
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".