What is "Equity"? Of Comparative Law, Time Travel and Judicial Cultures
Bibliographic record
Abstract
What is "equity"? Does it mean the same as the word "équité" in French ? Can the word "equity", used in an English or an American legal text, be translated readily by équité without being misleading? The answer to those two last questions is no. In the language of the common law, "equity" means something very specific and much more complicated than what we have in mind when we say équité in our civil law traditions. The present paper, adapted from a lecture given in Brasilia, attempts to shed some light on this awkward subject, as it compares the notion of équité in the French civil law tradition with the concept of equity indigenous to the English common law tradition. The mode of presentation used is that of the imaginary time machine: specialists of équité are thus interviewed one by one (Montesquieu, Portalis, Justice Magnaud) in chronological order, followed by English judges associated with the development of equity (Lord Coke, Chancellor Ellesmere and Lord Denning), Those historical figures use examples borrowed from their own time in order to illustrate the workings of équité/equity: in France the principle of liability for things and the abuse of rights theory, in England the trust, the estoppel and the injunction. As a conclusion, we discover that equity does not necessarily mean fair, and that équité has to express itself indirectly under the guise of judicial interpretation.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.049 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".