Personality traits and reading habits that predict absorbed narrative fiction reading.
Bibliographic record
Abstract
Even though the experience of narrative absorption has been studied extensively in recent years, relatively little is known about the personality traits of readers who regularly become absorbed while reading narrative fiction. The present study investigated which personality traits predict absorbed reading, with particular attention to the components of the Tellegen Absorption Scale (TAS; Tellegen & Atkinson, 1974) that do so. In an online survey study, introductory psychology students (N = 264) described their most memorable reading experience from the last year and elaborated on that experience by completing the Story World Absorption Scale (Kuijpers, Hakemulder, Tan, & Doicaru, 2014) and the Absorption-like States Questionnaire (Kuiken & Douglas, 2017). In addition, they were asked to complete several personality questionnaires (the TAS; Need for Cognition, Cacioppo, & Petty, 1982; Openness to Experience, DeYoung, Quilty, & Peterson, 2007; Emotional Coping, Stanton, Kirk, Cameron & Danoff-Burg, 2000) and to answer questions about their reading habits. The results indicate that (1) the chosen personality measures reflect closely related but distinct aspects of “global openness to experience,” (2) the effects of personality traits on absorbed reading are generally mediated by reading habits, and (3) sustained concentration and attentional flexibility are generic aspects of several absorption-like states predicted by trait measures of openness to experience. (PsycINFO Database Record (c) 2019 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 | 0.000 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".