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
The purpose of this paper is to show how corpus data can contribute to assessing explicit hypotheses about natural language just as experimental protocols can. The particular hypotheses tested concern the source of generalised conversational implicatures with quantifier some . Is the “some and not all” meaning of some a default interpretation of this item or a requirement of certain contexts? The defaultist approach (Levinson 2000, Chierchia 2004) would predict a preponderance of implicatures in the uses of some , whereas the contextualist approach (Sperber & Wilson 1986; Carston 1988, 2002) would predict that the implicature be found only with identifiable contextual triggers. The analysis of attested usage from the Bergen Corpus of London Teenage English (COLT) is shown to invalidate the former and to support the latter hypothesis. The workings of conversational implicatures are argued to be better understandable through corpus investigation than by recourse to decontextualized, self-fabricated, stock examples.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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