Can Differences in Word Frequency Explain Why Narrative Fiction Is a Better Predictor of Verbal Ability than Nonfiction?
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Bibliographic record
Abstract
Individuals who read more tend to have stronger verbal skills than those who read less. Interestingly, what you read may make a difference. Past studies have found that reading narrative fiction, but not expository nonfiction, predicts verbal ability. Why this difference exists is not known. Here we investigate one possibility: whether fiction texts contain more of the words typically evaluated by verbal ability measures compared to nonfiction texts. We employed corpus linguistic analyses to compare the frequency with which commonly tested SAT words appeared in both fiction and nonfiction texts, for 3 different corpora. Differences in SAT word frequency between the two genres were found to be negligible across all corpora. As a result, we conclude that there is little evidence that differences in word content between fiction and nonfiction texts can account for their differential relation to verbal ability. Other possible explanations are proposed for future research.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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