Bibliographic record
Abstract
Polarity (positive, negative) is one of the most fundamental concepts in the system of language and there are many expressions that are sensitive to polarity. For example, any in English and wh-mo in Japanese appear in negative contexts, but not in positive contexts. While previous studies have shown that polarity-sensitive expressions are a general phenomenon in languages, it has also become clear that there are variations in polarity-sensitive expressions. This volume explores the variations in polarity-sensitive expressions through comparisons between Japanese and other languages, such as English, German, Spanish, and Old Japanese, and examines the environments and contexts in which polarity-sensitive expressions occur, as well as the types of (cross-linguistic) variation allowed. The value of the present volume lies in its inclusion of research papers inquiring into various types of polarity-sensitive expressions, such as negative-, positive-, and discourse-sensitive polarity items as well as their variations. The research indicates new directions for the study of polarity-sensitive expressions in the fields of syntax, semantics, pragmatics, historical linguistics, corpus linguistics and psycholinguistics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.017 |
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".