Bibliographic record
Abstract
Abstract 1. On the status of the primitives It is interesting and surely non-coincidental that the semantic primitives proposed by NSM researchers include some of the most hotly-debated topics in the formal semantics literature. There is a large body of formal semantic research (too large to be cited here) on each of the following NSM primitives: indexical pronouns such as I and you, demonstratives like this, quantifiers such as something, all, many, and one, modals like can, propositional attitude verbs like know and think, adjectives such as good and bad, the predicates have and (there) is, the connectives because, when, and if. Other proposed primitives such as before, after, the same, like, and kind (of) have also been the subject of discussion and debate. Indeed, there may not be a single proposed semantic primitive which fails to strike formal semanticists as extremely complex. Thus, it is difficult for us to accept the NSM claim that primitives such as i, you, someone, this, think, and want are ‘simple words’ and that they are ‘intuitively comprehensible and self-explanatory’ (Durst, p. 2).
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".