Exploring the relation between the qualitative and quantitative uses of the determiner <i>some</i>
Bibliographic record
Abstract
This article attempts to repair the neglect of the qualitative uses of some and to suggest an explanation which could cover the full range of usage with this determiner – both quantitative and qualitative – showing how a single underlying meaning, modulated by contextual and pragmatic factors, can give rise to the wide variety of messages expressed by some in actual usage. Both the treatment of some as an existential quantifier and the scalar model which views some as evoking a less-than-expected quantity on a pragmatic scale are shown to be incapable of handling the qualitative uses of this determiner. An original analysis of some and the interaction of its meaning with the defining features of the qualitative uses is proposed, extending the discussion as well to the role of focus and the adverbial modifier quite. The crucial semantic feature of some for the explanation of its capacity to express qualitative readings is argued to be non-identification of a referent assumed to be particular. Under the appropriate conditions, this notion can give rise to qualitative denigration (implying it is not even worth the bother to identify the referent) or qualitative appreciation (implying the referent to be so outstanding that it defies identification). The explanation put forward is also shown to cover some's use as an approximator, thereby enhancing its plausibility even further.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".