The use of<i>any</i>with factive predicates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract While Negative Polarity Items are generally ungrammatical in veridical environments (* I said anything ), they are known to be found in factive environments that involve veridicality ( I regret you said anything ). There is however disagreement in the literature about the types of factive environments in which any is found. This paper proposes the first systematic large-scale survey of the use of any with factive predicates. Based on corpora totaling nearly 5 billion words, the paper establishes the relative frequency of any licensed by the different factive predicates (epistemic factives, as well positive, negative and counterexpectative emotives). Negative emotive factives (e.g. regret ) were found to license any 1.8 times more frequently than counterexpectative factives ( be amazed ), which license any 25.8 times more than do positive emotives ( be glad ). Emotive factives are associated with counterfactual preferences and expectations that make available a negative reading that licenses any . The examination of the data does not support a rescuing analysis that separates these occurrences of any from other licensed uses. On the contrary, the data show that any is licensed by at-issue meaning, as proposed by (Horn, Laurence. 2016. Licensing NPIs: Some negative (and positive) results. In Pierre Larrivée & Chungmin Lee (eds.), Negation and polarity. Experimental and cognitive perspectives , 281–305. Dordrecht: Springer.).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it