What's Your Pleasure? Exploring the Predictors of Leisure Reading for Fiction and Nonfiction
Bibliographic record
Abstract
Leisure reading is associated with several important educational and cognitive benefits, and yet fewer and fewer young adults are reading in their free time. To better study what drives leisure reading in undergraduates, we developed the Predictors of Leisure Reading (PoLR) scale. The PoLR investigates key predictors of leisure reading, namely reading motivations, obstacles, attitudes, and interests. We examined the PoLR’s ability to predict language skills in 200 undergraduates, both directly and indirectly via exposure to fiction and nonfiction texts. Language skills were measured with a diverse battery of tasks, including items from two sections of the Scholastic Aptitude Test. We found that greater intrinsic reading enjoyment predicts better verbal abilities, and this was often explained via exposure to fiction rather than nonfiction. In contrast, participants who reported reading due to extrinsic pressures typically had weaker verbal abilities, often explained by stronger associations with nonfiction. This pattern was observed across the raw correlations and in a series of path analyses. In sum, it was ‘reading enjoyment’ and ‘identifying as a reader’ that uniquely predicted better verbal abilities in our undergraduate sample. The importance of these findings are discussed in relation to fostering intrinsic reading enjoyment throughout the various stages of formal education.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".