Killing Ourselves: Depression as an Institutional, Workplace and Professionalism Problem
Bibliographic record
Abstract
Lawyers are frequent and consistent âwinnersâ of undesirable honorifics such as âmost depressed workers.â However, the undercurrent of unhappiness should not be ignored or hidden away by jokes told by lawyers about lawyers. In this article, the author proposes that depression is an institutional, workplace and professionalism problem in law. In Part II of the paper, the author analyzes professional codes of conduct as they relate to depression. Part III is devoted to the science of depression. Part IV examines the role of the institution, in particular law schools, to creating and reinforcing an environment that exposes individuals to developing depression. The âbusiness caseâ for why mental illness does have an impact, particularly in dollar terms, on a firmâs business, is analyzed in Part V. Lastly, Part VI is devoted to examining depression as a professionalism challenge.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.010 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".