You Were Selected for Your Lived Experience: A Love-Centered Evaluation from the Perspective of Teaching Assistants in an IPE Course in Higher Education
Bibliographic record
Abstract
Introduction. While teaching assistants with diverse backgrounds are subject to biased evaluations and perceptions of capacity due to race and gender, academic perspectives and emotional and psychological impacts of teaching on diverse teaching assistants is lacking. Objective. Applying post-qualitative methods of writing and autoethnography, three PhD level teaching assistants applied a love-centered program evaluation to assess whether they have what they need to facilitate an online asynchronous IPE on allyship. Methods. Over the course of five weeks, the teaching assistants met to discuss a need for this work, designed, and completed the program evaluation. Core evaluation activities included writing a series of self-addressed love letters and meeting for group reflections on the teaching experience and the content of the love letters. What Emerged. Systemic barriers to engaging left the teaching assistants feeling less effective than they had desired. The lack of training and ongoing support systems led to experiences of unanticipated harm. Conclusion. This evaluation aligns with research that suggests that structurally marginalized teaching assistants may require additional support to do their work without harm. Hiring and fairly compensating a small group of teaching assistants to design and deliver a curriculum that aligns with their values and is structured according to realistic learning outcomes may be one way to reduce the harm experienced by teaching assistants facilitating an allyship course.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".