Contextual Diversity and the Lexical Organization of Multiword Expressions
Bibliographic record
Abstract
Corpus-based models of lexical strength have questioned the role of word frequency in lexical organization. Specifically, closer fits to lexical behavior data on single words have been obtained by measures of contextual diversity, which modifies frequency by ignoring word repetition in context, semantic diversity, which considers the semantic consistency of contextual word distribution, and socially-based semantic diversity, which encodes the communication patterns of individuals across discourses (Adelman, Brown & Quesada, 2006; Jones, Johns, & Recchia, 2012; Johns, in press). The present work aimed at determining if diversity drives lexical organization also at the level multiword units. Normative ratings of familiarity for 210 English idioms (Libben & Titone, 2008) were predicted from contextual, semantic and socially-based diversity measures computed from a 55-billion word corpus of Reddit comments. Results confirmed the superiority of diversity measures over word frequency, suggesting that multiword idiomatic phrases show similar lexical organization dynamics as single words.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".