Variation, lexique et quantification les propriétés des adjectifs quantifieurs
Bibliographic record
Abstract
This thesis is concerned with the adjectival category in Universal grammar as well as the semantic and syntactic properties of measure adjectives in Quebec French. The relevant facts which are the object of this study are as in the following : J'ai grand de cuisine, long de corde, large de trottoir, épais d'eau, gros de chagrin, etc. (I have a big kitchen, a long rope, a large sidewalk, deep water, a big sorrow). The study tries to show that some adjectives in French, particularly measure adjectives, can borrow typical properties of another part of speech, namely those of a quantifier/determiner which provides an indeterminate or relative indication of an amount of substance. It is then observed that the adjective loses the characteristic properties of its lexical class, namely the morphological properties of gender and number agreement with N and becomes a mixed category. Our research also intends to illustrate how the semantic and syntactic restrictions which apply to these adjectives are part of a systematic knowledge listed in the mental lexicon of a universal grammar. Thus, the measure adjectives selected in these structures bear an unmarked value with a scalar type of opposition. Such an opposition is frequent in universal grammar, as can be observed in English: How old are you? /*How young are you? These adjectival quantifiers correspond in actual fact to the interpretation of the quantifier beaucoup in French but contrary to what is observed with beaucoup, the N which appears in the scope of the adjectival quantifier must almost always correspond to a mass and not to countable Ns (cf. J'ai beaucoup de terrains /* J'ai grand de terrains) (I have a lot/big of lands). It is then observed that these measure adjectives display nominal properties in grammar. Therefore, it is proposed that quantifier adjectives of measure are in fact defective nominal forms which bear a Quirky Case, as in Emonds (2000).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".