Knowledge and power in the academy: faculty and staff equity, diversity, and inclusion training in Canadian post-secondary institutions
Bibliographic record
Abstract
Most Canadian post-secondary institutions have publicly identified reconciliation and other equity, diversity, and inclusion (EDI) priorities in their goal statements and strategic plans. However, it is not clear how (or if) they are ensuring the institutional capacity exists to make the type of transformative changes these goals imply. It is argued here that a critical, yet overlooked component of this capacity building is meaningful EDI training for faculty and staff. The literature argues that those in positions of power and privilege have been culturally conditioned to not see violence and oppression in our institutions. If this is so, it becomes vital that EDI training initiatives provide an opportunity to critically analyze the impact of personal power and power embedded in institutional structures. However, the review also revealed the most common type of EDI training offered is superficial and does not support the development of a more critical lens through which to interrogate power. As the evidence to support these concerns has been largely anecdotal, this study set out to gather a more comprehensive, descriptive data set that provides a snapshot of faculty and staff EDI training initiatives across Canadian post-secondary institutions. This study takes a pragmatic approach to data gathering and uses a mixed methods approach to support the investigation of complex systems and processes. Based on issues raised in the literature review, a national survey was designed to support the gathering of large amounts of both quantitative and qualitative (descriptive) data from those doing “EDI work”. A focus group was then held that allowed for a deeper examination of key issues raised in the survey. Findings echoed concerns that most EDI training for faculty and staff is superficial and does not get at deeper issues related to power and equity. The analysis also revealed that differing perceptions of safety may be a critical factor impacting the perceived need for EDI training and the type of training typically offered. Galtung’s Violence Triangle model frames the research as it broadens the concept of “violence” and helps elucidate power imbalances and the mechanisms that both create and maintain them.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.039 | 0.020 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".