Bibliographic record
Abstract
This paper examines complexities of being authentic in higher education teaching by sharing personal narrative. Existing scholarship champions authentic teaching. It overlooks the potential emotional impact on students and educators when sharing highly personal or traumatic narratives, including those involving illness and loss. Drawing on personal experiences of sharing (and not sharing) personal narratives, including significant personal loss, I examine boundary conditions of authenticity and vulnerability in the classroom, and argue for a more responsible approach. Instead of sharing raw experiences that may impose undue emotional burdens, educators may achieve better outcomes by sharing wisdom we have gained from our wounds. This approach reconciles our privileged position as educators, our own need to share, and the emotional weight that sharing places on us and students. I share practical advice on how to share responsibly and retain authenticity without sacrificing the emotional well-being of ourselves and students.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.024 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".