Bibliographic record
Abstract
HOW DO YOU NEGOTIATE TRAUMA AND EMOTIONS IN YOUR CLASSROOM? Posing this open-ended question to law professors not only begets more questions, but also often elicits a reflexive retort: law professors dare not present themselves as mental health experts and law schools have mental health resources for students having difficulties. The difficulty of this approach is that in 2021, most law students are no longer willing to accept that their legal education must suppress emotions, including trauma.2 For classrooms where professors may be less comfortable with emotional discussions, they may find themselves challenged and perhaps even feel obstructed from teaching their subject matter with the freedom and expertise it deserves. Are we simply dealing with an overly sensitive generation? Or are we being pushed to make overdue changes that will improve legal teaching, legal education, and eventually the profession? I would propose that identifying and trying a combination of simple strategies (some suggested below) that better acknowledge trauma (whether or not the professor chooses to use that term, and whether or not the class is a small seminar or large lecture) is to everyone’s advantage in today’s law school.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.025 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".