Equity, diversity, and inclusion in Canadian colleges: examining definitions and unveiling perceptions
Bibliographic record
Abstract
Equity, Diversity, and Inclusion (EDI) through strategic policy development has been at the forefront of institutional change, especially within the higher education sector. Canadian colleges have a large equity-seeking student population due to their open admission structure, but despite this, there are implicit biases and actions that impede students’ learning experience. Limited literature exists around how Canadian colleges have approached EDI policy development; thus, this paper initiates the unpacking of the evident policy – practice disconnect by asking: ‘How do colleges understand equity, diversity, and inclusion as articulated in policy documents?’ To address this, the study employs Critical Policy Analysis (CPA) as its main research method to deconstruct the narratives found within purposefully sampled documents including: 1) EDI policies and procedure; 2) institutional multi-year strategic plans; and 3) EDI-based plans and performance reports. Results indicate that colleges tend to be selective in their usage of EDI definitions. There is also a tendency to use ambiguous language around EDI, which makes it difficult to mobilise knowledge effectively and approach equity directly. Policies often fail to address the privileges held by certain positionalities, reinforcing existing power structures rather than challenging them. The significance of this research is in its contribution to enhancing a theoretical understanding of how knowledge supports policy, as well as informing the future development of constructive EDI policies in higher educational institutions.
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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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.033 | 0.026 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| 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".