Towards a critical co-construction of equity communities of practice in education
Bibliographic record
Abstract
Students in Canada have unequal access to safe environments and learning in schools, which impacts their participation in education and their achievement of educational goals. Equity, diversity, and inclusion (EDI) courses for future educators are one way to help them see that the benefits of schooling are not equally available to all students. The authors describe how post-secondary students, who were members of equity-seeking communities and their allies, worked together with instructor guidance to co-create EDI courses. The students were already familiar with the fully online learning community model (FOLC) where student voice and agency feature prominently. As the students co-designed new EDI courses, the critical co-construction of equity model was developed. The model is anchored in human rights and relies on a shared spirit of equity humility. The model recognizes the need for student safety as well as the necessity of potentially uncomfortable conversations. While, in the past, equity teaching tended to focus on distinct aspects of oppression in society as individual topics, the co-construction of equity model relies, instead, on building bridges of equity concepts that cross oppressions. These cross-equity understandings can help future educators see the importance of dismantling oppression and rebuilding safer and more inclusive learning spaces in education.
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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 | 0.030 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.044 | 0.127 |
| Scholarly communication | 0.024 | 0.015 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.005 | 0.007 |
| 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".