Building Faculty Efficacy in Teaching: Bringing Equity and Decolonization into Focus
Bibliographic record
Abstract
This article shares the results of a research study designed to track faculty efficacy with integrating equity, inclusion and decolonization into their teaching. In response to student priorities, broad national policy priorities, and the lived experiences of researchers, this seven-person multi-department team followed a set of 16 faculty through a set of interventions and gathered a range of data to discern their shifts in awareness, intention and practice. Four major resources were made available to participants (a 4-hour training, an expansive self-assessment, syllabus revisions and development of an action plan). Five data collection activities were implemented (a pre/post efficacy survey, the self-assessment survey results, action plans for immediate and future practices, syllabus revisions, and focus groups). Promising results signal that targeted training, and relevant resources can manifest instructors’ skills and confidence to teach in ways infused with anti-racism, decolonization, cultural responsiveness and transformative learning.
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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 | 0.028 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".