Bibliographic record
Abstract
It will surprise no one to hear that laws are often vague, and, moreover, that laws are vague for a variety of reasons.Some vagueness results from specific intentions of users of legal language.Laws may be poorly drafted, or drafted using general terms intended to capture a wide yet unspecified range of affairs.Vagueness of these kinds is, for the most part, noticeable yet bearable.Poor drafting can be fixed, and open-textured laws can sometimes be made more specific if their application proves troublesome.Yet in the trade-off between specificity and openness, a more peculiar kind of vagueness seems intrinsic to the nature of some legal norms.Their open texture cannot be sharpened to a univocal statement of what they require: they are indeterminate, leaving borderline cases where judges lack legal resources to resolve a dispute one way or another.At least part of the difficulty in handling norms of this sort comes from the mixture of reasons for their vagueness.When we observe, for example, that the Canadian Charter of Rights and Freedoms contains a vague provision permitting 'reasonable limits' 1 on fundamental rights and freedoms, we recognize also that mere understanding of the meaning of 'reasonable' goes only so far in dissolving its vagueness.'Reasonable limits' are not just any artefacts of language use.They are normative standards, notoriously elastic and insusceptible to univocal and final statement in application to concrete cases.Here the real trouble begins: there is at least the air of paradox in the contrast between the fact of vagueness in laws and the opposed determinacy demand intrinsic to the rule of law -the requirement that laws be framed in a way that makes them capable of being obeyed.When has meaning been stretched too far?What justifies judicial setting of borders on the application of indeterminate propositions of law?After all, if 'reasonable limits' have no determinate edges, our most fundamental rights and freedoms have no inviolable borders, and the rule of law has failed.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| 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".