Embedding equity and inclusion in universities through motivational theory and community‐based conservation approaches
Bibliographic record
Abstract
Despite widespread plans to embed justice, equity, decolonization, indigenization, and inclusion (JEDII) into universities, progress toward deeper, systemic change is slow. Given that many community-based conservation (CBC) scholars have experience creating enduring social change in diverse communities, they have transferable skills that could help embed JEDII in universities. We synthesized the literature from CBC and examined it through the lens of self-determination theory to help identify generalizable approaches to create resilient sociocultural change toward JEDII in universities. Fostering autonomous motivation (i.e., behaving because one truly values and identifies with the behavior or finds behavior inherently satisfying) is critical to inspiring enduring change in both CBC and JEDII. Based on theory and our examination of CBC, we provide 5 broad recommendations that helped motivate behavioral change in a way that was self-sustained (i.e., even without external rewards or pressure). Guiding principles support autonomy by creating meaningful choice and different entry points for JEDII; prioritising relationships; designing payment programs that enhance autonomous motivation; developing meaningful educational opportunities that are relevant, timely, relational, and authentic; and creating institutional change by focusing efforts on critical moments.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".