Bibliographic record
Abstract
The post-secondary education sector is increasingly incorporating equity, diversity, and inclusion (EDI) frameworks into its institutions. This transition from traditional concepts of affirmative action and employment equity to a decolonization, equity, diversity, and inclusion (DEDI) model was very much in development both at administrative and faculty levels during my stint as Co-Chair of the Joint Committee on Administration of the Agreement (JCoAA), representing a large faculty association. In regular meetings with Labour Relations, representing university administration, conceptual perspectives differed, objectives needed to be agreed upon, and goals compromised. This paper explores the broader model of justice, equity, diversity, decolonization, and inclusion (JEDDI) and the absolute importance of such a perspective for the higher education sector and labour market in general. Implementing and actualizing JEDDI is important as universities continue to diversify. Utilizing such frameworks can assist in assuaging tensions regarding academic freedom, governance, and labour practices.
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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.033 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".