Mondialisation des engagements d’Équité, Diversité et Inclusion à l’université et intégration d’une posture anti-raciste dans un cursus de français L2
Bibliographic record
Abstract
In recent years, faculty members have heard numerous calls to review their research, teaching and administrative practices from the perspective of a mission known as Equity, Diversity, Inclusion, or under the acronym EDI, to which the terms Decolonization and Social Justice are sometimes associated. These calls have found a varying support depending on the disciplines, institutions and countries, where teachers and researchers have undertaken to integrate these objectives into their work. In Canada, following the report of the Truth and Reconciliation Commission (2015), universities developed initiatives which claimed to decolonize and indigenize the curriculum. This article questions the impact of a higher education course, which studied the media coverage of racialized activists in France, as taught in this context. After a brief presentation of the institutional use of the word 'decolonization' and its critiques, the article describes the parameters of the development of this course and its implications in terms of engagement and allyship. It then questions the dimension of the latter considering that the course is taught in L2 French, a language whose presence around the world finds its origins in a colonial and imperialist history and in the discrimination of indigenous, creole, regional and vernacular languages. Finally, I present the argument that positionality is a crucial part of the decolonial reflection and the critical and anti-racist pedagogy, for which epistemological points of view must be openly presented and historicized.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".