Lawyering in Hard Places: Comparative Dispatches from the Margins of Legality
Bibliographic record
Abstract
What do Lawyers do when Legality Unravels?Lawyering is nowhere easy, but in liberal democracies governed by the rule of law, we know what effective lawyers look like: part of an independent bar, these professionals keep clients within the bounds of the law, persuade judges to win disputes in court, and promote the public interest via pro bono work.But not all lawyers find themselves in such favorable contexts.What of lawyers working in hard places at the margins of the rule of law or where legality unravels into violence?Consider two examples and ask yourself: where are we, and how do these contexts challenge conventional lawyering?In the first example, the president of a national supreme court under attack by a hostile government travels abroad to meet with fellow judges and deliver a cry for help: in their home country, the rule of law is under siege:In the second example, after packing his country's supreme court, a corrupt ex-president lawyers up and goes for the kill.Preparing to regain executive office, For an expanded version of this article, see "Lawyering in Hard Places: Comparative Dispatches from the Margins of Legality,"
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.081 | 0.003 |
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".