Reassessing word frequency as a determinant of word recognition for skilled and unskilled readers.
Bibliographic record
Abstract
The importance of vocabulary in reading comprehension emphasizes the need to accurately assess an individual's familiarity with words. The present article highlights problems with using occurrence counts in corpora as an index of word familiarity, especially when studying individuals varying in reading experience. We demonstrate via computational simulations and norming studies that corpus-based word frequencies systematically overestimate strengths of word representations, especially in the low-frequency range and in smaller-size vocabularies. Experience-driven differences in word familiarity prove to be faithfully captured by the subjective frequency ratings collected from responders at different experience levels. When matched on those levels, this lexical measure explains more variance than corpus-based frequencies in eye-movement and lexical decision latencies to English words, attested in populations with varied reading experience and skill. Furthermore, the use of subjective frequencies removes the widely reported (corpus) Frequency × Skill interaction, showing that more skilled readers are equally faster in processing any word than the less skilled readers, not disproportionally faster in processing lower frequency words. This finding challenges the view that the more skilled an individual is in generic mechanisms of word processing, the less reliant he or she will be on the actual lexical characteristics of that word.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".