FROM UNDERSTANDING TO ACTION: AN EXAMINATION OF TRANSFORMATIVE LEARNING IN ASYNCHRONOUS ONLINE EQUITY, DIVERSITY, AND INCLUSION TRAINING FOR FIRST- TIME TEACHING ASSISTANTS
Bibliographic record
Abstract
To better support TAs in creating inclusive classrooms, three (3) online, asynchronous modules were developed and implemented to introduce first-time TAs to core concepts of equity, diversity, and inclusion (EDI) - Foundational EDI Language, Power, Privilege, and Positionality and Interrupting Bias. Over 100 1st time TA completed each module, with 80-90 providing feedback on their experience upon completion. A preliminary review of this feedback highlighted three major themes: 1) building awareness and knowledge, 2) applying EDI concepts to teaching practice & identifying actions, and 3) feeling empowered to act. Overall, TAs expressed strong development of awareness and new knowledge of key concepts such as equity and positionality. Although TAs were also able to identify and state the value of applying these concepts to their teaching practice, many expressed the sentiment of still feeling uncomfortable to act within “real-life” situations. Future iterations of such training could seek to address this through structured opportunities for analysis and feedback of reflective responses.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".