Bibliographic record
Abstract
Abstract The traditional definition of permission as the dual of obligation oversimplifies its many applications, often yielding undesirable consequences. Recent literature recognizes the need to distinguish various types of permissions but often overlooks potential preferences associated with them. In contrast, the Sanskrit philosophical school of Mīmāṃsā refutes the interdefinability of deontic concepts, and asserts that permissions always refer to less desirable actions (‘better-not’ permissions), and are exceptions to prohibitions or negative obligations. This article analyzes the concept of Mīmāṃsā permission, compares it with contemporary theories and formalizes it while carefully preserving its essential characteristics. We transform Mīmāṃsā’s reasoning principles for permission into Hilbert axioms and introduce neighbourhood semantics, incorporating ceteris-paribus preferences. The resulting logic is evaluated against various paradoxes from contemporary deontic logic and applied to a scenario from Sanskrit jurisprudence.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".