“It's Kind of My Responsibility”: An Analysis of the Current EDI Discourse in Canadian STEM Fields and its Potential and Limitations to Contest Intersectional Discrimination
Bibliographic record
Abstract
Since 2019, equity, diversity, and inclusion have become institutional priorities for Canadian funding agencies and universities under the acronym EDI. Here, we examine for the fields of science, technology, engineering, and mathematics (STEM) how the current EDI discourse unfolds in scientists’ understandings as EDI construct. This study presents data collected through 18 online interviewswith researchers in STEM fields across Canada. For our analysis we apply critical discourse analysis and the matrix of domination. Four themes emerge from our data regarding STEM researchers’ understanding of and experience with the EDI construct: (a) EDI astrainable knowledge, (b) EDI as human resources/managerial issue, (c) EDI as assessable performance, and (d) EDI as individual initiative/lonely endeavour. Our findings suggest that the EDI discourse increases the awareness of the underrepresentation ofgroups in STEM fields. However, most interview participants demonstrate an essentialist understanding of identity decontextualized from institutional and structural processes of difference making along axes of gender, race, class, and body, amongst others. This critical discourse-analytical work contributes to an intersectional, power-acknowledging understanding of EDI in Canadian highereducation.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.058 | 0.047 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 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".