The Power of 'Wonder' by R. J. Palacio to Trigger Young Readers’ Emotionally Literate Responses
Bibliographic record
Abstract
Readers’ emotions are naturally blended with their cognitive abilities in the transaction with literary texts. From the perspective of emotional intelligence, an emotionally literate reader will be able to read beyond the surface of the text and make inferences regarding shades of feelings, their causes and effects. The purpose of the present study was to observe whether there is any correlation between the emotional intelligence profile of young readers and their abilities to identify the emotional input in literary texts and its impact on themselves. The study was carried out with the participation of 72 students in the first year at the Faculty of Letters in Iași. It consisted in three stages and relied both on quantitative and qualitative data collection. In the first stage, the students filled in a Reading literary texts – Self-report questionnaire; in the second stage they filled in the How Empathetic are You? (The Toronto Empathy Questionnaire, TEQ) (“How Empathetic”) and in the third stage they were given excerpts from the book Wonder by R. J. Palacio in order to check whether the self-reported emotional literacy skills were at work when approaching a literary text. Approximately half of the students (30) offered to watch the film prior to class discussion and work. The answers were compared with the results of the self-reported questionnaires and a natural and fairly consistent correspondence between the profiles of readers in terms of empathy in general and the empathy felt with regard to the fictional characters together with a good command of emotion vocabulary could be observed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".