The grammar of ‘non-realization’
Bibliographic record
Abstract
Abstract On the basis of cross-linguistic data from both genetically and geographically related and unrelated languages, in this article we argue that the linguistic phenomena usually referred to as the avertive, the frustrative and the apprehensional belong not to three but to five – semantically related, and yet distinct grammatical categories, all of which involve different degrees of non-realization of the verb situation in the area of Tense-Aspect-Mood: apprehensional, avertive, frustrated initiation, frustrated completion, inconsequential. Our major goal here is to account for these grammatical categories in terms of an adequate model of linguistic categorization. For this purpose, we apply the notion of Intersective Gradience (introduced for the first time in the morphosyntactic domain in Aarts ( 2004 , 2007 ) to the morphosemantic domain. Thus the present approach reconciles two major approaches to linguistic categorization: (i) the classical, Aristotelian approach and (ii) a more recent, gradience/fuzziness approach.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".