Beyond diversity management : a pluralist matrix for increasing meaningful workplace inclusion
Bibliographic record
Abstract
Despite rapidly burgeoning diversity in the Canadian workforce, and demonstrable gains to be made as a result of increasing inclusion, organizations still struggle to create meaningfully inclusive workplaces. The traditional diversity management model has largely failed to fix this longstanding problem. A variety of research has identified successful strategies for increasing inclusion across disciplines such as social psychology, critical management studies, systems theory, and universal design. However, these overlapping strategies, as well as the commonalities of underlying structure, go unseen due to ideological and disciplinary siloing. Working from a foundation of theoretical pluralism, I present two linked ideas in this paper. First, I propose and justify a shift in language from the counter-productive diversity management towards meaningful inclusion. Second, using multi-disciplinary research I identify successful, broadly-applicable strategies for enhancing meaningful inclusion in the workplace, and describe an inclusion matrix of best practices that creates a practical road map organizations can use to enhance meaningful inclusion.
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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 | 0.048 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.039 | 0.045 |
| Scholarly communication | 0.026 | 0.021 |
| Open science | 0.004 | 0.046 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".