Content matters: Measures of contextual diversity must consider semantic content
Bibliographic record
Abstract
Measures of contextual diversity seek to replace word frequency by counting the number of contexts in which a word occurs rather than the raw number of occurrences (Adelman, Brown, & Quesada, 2006). It has repeatedly been shown that contextual diversity measures outperform word frequency on word recognition datasets (Adelman & Brown, 2008; Brysbaert & New, 2009). Recently, Hollis (2020) has questioned the importance of contextual diversity by demonstrating that when other variables of contextual occurrences are controlled for, diversity accounts for relatively small amounts of unique variance over word frequency. However, the analysis of Hollis (2020) did not take into account the semantic content of the contexts that words occur in. Johns, Dye, and Jones (2020) and Johns (2021) have recently shown that defining linguistic contexts at larger, and more ecologically valid, levels lead to contextual diversity measures that provide very large improvements over word frequency, especially when implemented with principles from the Semantic Distinctiveness Model of Jones, Johns, and Recchia (2012). Across a series of simulations, we demonstrate that the advantages of contextual diversity measures are dependent upon the usage of semantic representations of words to determine the uniqueness of contextual occurrences, where unique contextual occurrences provide a greater impact to a word’s lexical strength than redundant contextual occurrences.
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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 | 0.007 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".