Responsibility for Another’s Debt: Suretyship, Solidarity, and Imperfect Delegation
Bibliographic record
Abstract
Legal evolution is often achieved by taking a fresh look at venerable institutions whose interpretation has become thwarted, constricted, or stale. Presumptions established to protect debtors and sureties at articles 1525 and 2335 of the Civil Code of Québec have prevented jurists from borrowing freely from the rules of solidarity and suretyship. Where one person is undoubtedly responsible for the debt of another, even in the absence of a suretyship agreement, the author argues it should be possible to apply the law of suretyship by analogy. Where two persons are each liable to perform the same obligation in full, it is likewise appropriate to apply the rules of solidarity. The author’s analysis proceeds in three parts: an introduction of the basic structure of suretyship and solidarity (Part I), a discussion of important differences in the law of suretyship and solidarity (Part II), and an argument that the solidarity and suretyship models should be used to illuminate analogous complex relations where multiple persons are responsible for the same debt (Part III). More specifically, in the situation of imperfect delegation, where a person assumes liability to a creditor for payment of a debt owed by another, but the original debtor is not discharged and remains liable in case of non-payment by the new debtor, it is appropriate to apply by analogy the law of suretyship.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.038 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".