Positive polarity items: an illusion of ungrammaticality
Bibliographic record
Abstract
Negative Polarity Item (NPIs) produce an illusion of grammaticality in some contexts with negation. Many approaches to modelling the NPI illusion propose that it is driven by the processor's attempt to link an NPI to a negative element. We investigate an illusion effect observed with Positive Polarity Item (PPIs), another class of polarity sensitive element. While NPIs must be licensed by a negative element, PPIs are anti-licensed by negative elements. We find an illusion of ungrammaticality for PPIs in environments where an illusion of grammaticality is observed for NPIs. Thus, we argue there is a general polarity illusion. We find that several accounts of the NPI illusion either predict this PPI illusion or can capture this effect with a straightforward extension. The approaches which are able to predict this effect share a reliance on structural representation, highlighting the importance of both the licensing features of polarity items and the structural detail in sentence processing representations.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".