Bibliographic record
Abstract
Abstract In this article, we consider legal notices of various forms, including imperative, indicative, and non-sentential. We argue that these convey various illocutionary forces depending on their particular content. In particular, those that prohibit actions — unlike laws that do so — typically have “directive” illocutionary force, with different linguistic classes of legal notices achieving this force through different means, given their distinct linguistic properties. We propose a “bare phrase” treatment of non-sentential notices, whereby these are underlyingly and not just superficially non-sentential; and a semantic treatment in terms of Discourse Representation Theory, which perspicuously describes their contribution to interpretation. Finally, we argue that assigning such sparse syntactic and semantic representations to non-sentential notices has conceptual and empirical advantages over analyses that posit richer underlying structure, capturing a broader range of data, including patterns involving default case and the absence of articles, and minimizing the need to posit linguistic ambiguity.
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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 | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.001 | 0.003 |
| 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".