Bibliographic record
Abstract
Analogy is an inherently fragile form of argument, as it derives the conclusion from similarity, while overlooking dissimilarities. Yet law fundamentally depends upon analogical reasoning to ensure consistency and predictability in its rulings. This entanglement of fragility and necessity compels the legal traditions, both Islamic and contemporary, to articulate a theory of legitimacy that justifies analogy when applied in law. The most successful explanation among different considerations, I argue, is the one that is anchored in logic. This logical grounding is a shared feature among contemporary legal theorist Scott Brewer, the informal logician Douglas Walton, and the 12th-century Muslim jurist-logician al-Ghazzālī, as all three insist that the justification of legal analogy is logical. This paper aims to trace the thematic contours of these logical frameworks in order to demonstrate, drawing primarily on al-Ghazzālī’s two works: al-Mustaṣfā fī Uṣūl al-Fiqh and al-Muntaḥal fī al-Jadal, that al-Ghazālī’s model of analogical reasoning effectively integrates key elements of Brewer’s abduction model and Walton’s model of defeasible argument.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".