Bibliographic record
Abstract
Not just any statements (Natalie won’t drink just ANY wine from France) carry an intonational contour. Previous studies have characterized the contour as fall-rise [D. Robert Ladd, The structure of intonational meaning (1980)], but more intricate acoustic analysis that considers the semantics of the construction has not been performed. Preliminary data shows that for speakers of Canadian English, the contour contains a rise-fall and stress accent on any, followed by a rise on the sentence-final syllable. If the stressed rise-fall on any indicates a topic accent, then the meaning of the construction can be calculated semantically: the presence of the topic accent indicates a disputable topic (in this case, Which wines will Natalie drink?) [following Daniel Buering, Linguistics and Philosophy 20, 175–194 (1997) for German]. This operation narrows the domain of any wine from all wine from France to some smaller amount perhaps Natalie only drinks expensive cabernets. Furthermore, the same intonational contour occurs in other quantificational statements, like ALL gamblers aren’t addicted. Here, the disputable topic is: How many gamblers are addicted? Again, the intonation serves to narrow the domain to a subset of all the gamblers. Thus, documenting this intonational contour identifies a more general semantic process.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".