Delivering a collaborative, discipline-specific, and equity, diversity, and inclusion-centered teaching assistant training program in the life sciences
Bibliographic record
Abstract
A growing number of Canadian universities are implementing teaching assistant (TA) training programs designed to support graduate student teaching. At the University of British Columbia (UBC), the Life Sciences Institute (LSI) hosts one of several provost-funded, discipline-specific TA training programs on campus. At its core, the LSI TA Training Program is a partnership between senior TAs and faculty from multiple departments. Together, we design and deliver a TA training curriculum tailored for teaching in the life sciences that centers equity and inclusivity, while building a supportive community of emerging educators. In this paper, we provide a brief historical overview of the LSI TA Training Program and describe our embedded values and initiatives, including a flagship TA training "boot camp," workshops, social celebration events, and a year-round mentorship initiative. We also present evidence showing that participation in our TA training program increases confidence in evidence-based teaching strategies, including creating inclusive, equitable classroom environments. We also discuss lessons learned in developing and sustaining such a cross-departmental TA training initiative, highlighting the benefits of faculty-student collaboration and our approaches to ensure our program is sustainable while adapting to evolving TA needs. Our experiences highlight that grounding discipline-specific TA training in inclusive teaching prepares new TAs with relevant, practical skills and supports their confidence to be inclusive educators in the life sciences.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".