Individual differences in literary reading: Dimensions or categories
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.005 | 0.001 |
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