Determining the importance of frequency and contextual diversity in the lexical organization of multiword expressions.
Why this work is in the frame
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Bibliographic record
Abstract
Corpus-based models of lexical strength have called into question the role of word frequency as an organizing principle of the lexicon, revealing that contextual and semantic diversity measures provide a closer fit to lexical behavior data (Adelman et al., 2006; Jones et al., 2012). Contextual diversity measures modify word frequency by ignoring word repetition in context, while semantic diversity measures consider the semantic consistency of contextual word occurrence. Recent research has shown that a better account of lexical organization data is provided by socially based measures of semantic diversity, which encode the communication patterns of individuals across discourses (Johns, 2021b). While most research on contextual diversity has focused on single words, recent corpus-based and experimental evidence suggests that an integral part of language use involves recurrent and more structurally complex units, such as multiword phrases and idioms. The aim of the present work was to determine if contextual and semantic diversity drive lexical organization at the level of multiword units (here, operationalized as idiomatic expressions), in addition to single words. To this end, we analyzed normative ratings of familiarity for 210 English idioms (Libben & Titone, 2008) using a set of contextual, semantic, and socially based diversity measures that were computed from a 55-billion word corpus of Reddit comments. The results confirm the superiority of diversity measures over frequency for multiword expressions, suggesting that multiword units, such as idiomatic phrases, show similar lexical organization dynamics as single words. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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