Bibliographic record
Abstract
The aim of this paper is to provide an analysis of the positive effect that modification has on the distribution of noun phrases in otherwise illicit environments. I focus on de nominals in French. By focusing on these nominals, whose distribution is altered by the addition of modifiers, the paper shows that modifiers can do much more than simply modify: they can change the syntactic and semantic status of a noun phrase. The licensing property of modifiers is an intriguing topic and has not been greatly discussed in the literature. I argue that modifiers can come to play the role of determiners in French as long as they are accompanied by a head de , which is the spell-out of a Cardinal head (see Lyons 1999). My proposal goes back to an old idea put forward by Damourette & Pichon (1911–1940) according to which, in modified contexts, de functions as one half of the article while the adjective functions as the other half. More generally, articles in French are seen as dual entities comprising of a specifier and a head. In the absence of the determiner les , an adjective can raise to the specifier of CardinalP. This is achieved via phrasal rather than head movement.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".