Diversity of worldviews: reflections about diversity, equity, and inclusion
Bibliographic record
Abstract
The co-authors, a racialized woman from India and a Nanualco (Indigenous Mesoamerican peoples whose ancestral territory encompasses central Mexico to the tip of Costa Rica) man from Nahua-Pipil (another name for Nanualco people) territory in El Salvador are encountering increasing discourse and action about diversity, equity, and inclusion (DEI) within academia in Canada. The DEI initiatives rooted in the Eurocentric worldview are found to reproduce the same inequities they are attempting to address. Given this context, the co-authors engaged in a process of dialogic reflections and co-learning over a period of 6 years to critically examine the notions of DEI considering their lived experiences, the domination of Eurocentric worldview, and an emerging understanding of their own ancestral worldviews. The co-authors express the need to dismantle the domination of the Eurocentric worldview and to expand the notions of DEI by honouring diverse worldviews within and beyond academia.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.039 | 0.067 |
| Scholarly communication | 0.022 | 0.013 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.005 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 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".