Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it