This isn’t plug and play: intersectionality, Indigeneity, and EDID work
Bibliographic record
Abstract
For some, Equity, Diversity, Inclusion, and Decolonization (EDID) work is new, and they are working to catch up. For others, attention to EDID work occurring is because of the many years of labour involved, and it is not a new area or vocation. Much like Indigenization, which is being treated as plug and play when it is its own discipline, EDID is developing in a similar fashion. EDID committees are becoming part of administrative work within institutions, just as Indigenization and reconciliation committees did a few short years ago in many spaces. Institutions must populate these communities, and with their creation, conversations are developing surrounding questions of who is qualified to work in the area? This article explores some of the nuanced differences to consider when working with EDID and Indigenization space. This discussion means to serve as a contribution to the broader discussion taking place.
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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 | 0.021 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.029 | 0.125 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".