Individual differences in literary reading: Dimensions or categories
Bibliographic record
Abstract
Literary text reading has long been a subject of empirical research. Various measures of reader differences and reader typologies were suggested, with the most prominent being studies of literary expertise, and studies employing Literary Response Questionnaire (LRQ; Miall & Kuiken, 1995). Literary expertise is difficult to define and fails to account for potential differences within non-experts. LRQ and similar dimensional approaches neglect the possibility that a salient reader typology does exist. The main goal of this study is to test whether a salient reader classification can be formed based on participant responses to questionnaires and to test how this classification corresponds to self-reported reader expertise. Based on responses from 741 participants (78.41% female, mean age = 24.31), we test the factor structure of LRQ in its Serbian translation and find moderate, acceptable fit. We also present our own Receptiveness to Literature Questionnaire (UPK) with two factors named Thorough Reading and Reading for Pleasure. Finally, we discuss relations between LRQ and UPK, offer classifications of readers formed on participant factor scores, and test the congruence between these classes and self-reported participant expertise. Our results indicate that a dimensional approach should be favored over forming categories of readers.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".