Disentangling contextual diversity: Communicative need as a lexical organizer.
Bibliographic record
Abstract
Contextual diversity (CD; Adelman, Brown, & Quesada, 2006) modifies word frequency by ignoring word repetition in context. It has been repeatedly found that a CD count provides a better fit to lexical organization data than does word frequency (e.g., Adelman & Brown, 2008; Brysbaert & New, 2009). The importance of CD has been interpreted with the principle of likely need, adapted from the rational analysis of memory (Anderson & Schooler, 1991), which states that words that have been used in many past contexts are more likely to be needed in a future context. Central to the cognitive mechanisms of computing likely need is a definition of linguistic context itself. Typically, linguistic context is defined by relatively small units of language, such as a document within a corpus. However, recent research has demonstrated that larger definitions of context, some spanning tens or hundreds of thousands of words, provide a better accounting of lexical organization data (Johns, Dye, & Jones, 2020). This article attempts to redefine the notion of linguistic context by using socially based contextual measures, derived from the online communication patterns of hundreds of thousands of individuals from the discussion forum Reddit, consisting of over 55 billion words. Multiple count-based and semantic diversity models of contextual diversity were derived from this data. The results demonstrate that the communication patterns of individuals across discourses provides the best accounting of lexical organization data, indicating that classic notions of using local linguistic context to update a word's strength in the lexicon need to be reevaluated. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".