Finding frequency effects in the usage of NOT collocations in American Sign Language
Bibliographic record
Abstract
This study explores whether American Sign Language (ASL) users exhibit frequency effects on two-sign combinations as observed in spoken languages. Studies on spoken languages have demonstrated that frequency of usage influences the emergence of grammatical constructions; however, there has been less investigation of this question for signed languages. To examine frequency effects in ASL, this study analyzes patterns of a grammatical manual negation morpheme glossed as NOT produced sequentially with other signs. Findings reveal that NOT is produced with specific signs, demonstrating that the grammaticalization of NOT increases as frequency does in ASL collocations. The analysis shows that a few signs are highly phonologically fused with the negation marker, providing emerging evidence that these collocations have experienced chunking , as they are schematic, fused constituent structures in ASL. Given frequency effects found in the study, chunking appears to be a domain-general cognitive processing mechanism independent of modality effects.
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".