Bibliographic record
Abstract
Abstract There has been an explosion of interest in “Equity, Diversity, and Inclusion” (EDI) – also referred to as DEI among other acronyms. On the one hand, this management trend has the potential to draw attention to the ways in which organizational practices and climates can be transformed to have a positive impact on the underrepresentation of women and other marginalized and excluded groups in the workplace. On the other hand, there may be real consequences for women as EDI replaces other concepts such as women's rights, gender equality, affirmative action, employment equity, gender discrimination, etc. This chapter applies a gender lens to the EDI concept and management policy and practice. It juxtaposes EDI's emergence with the lack of progress on gender equality that is observed and measured in many regions of the world and highlights several critiques that may explain this lack of progress. It also identifies what EDI policies and practices need to take into consideration to better address gender inequality in the workplace. Legal approaches are discussed along with a list of potential areas of research on EDI and gender equality to determine the best path forward for making concrete progress on true equality for women 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".