Exploring reader responses to young adult literature in the Malaysian English language classroom
Bibliographic record
Abstract
This article presents the results of a study exploring the reader-responses of Malaysian young adults (YAs) to the literature texts used in Malaysian secondary schools, Dear Mr. Kilmer by Anne Schraff, Captain Nobody by Dean Pitchford, and Sing to the Dawn by Minfong Ho. The study aimed to determine the extent to which the YAs found these texts engaging and relevant, and how they identified aspects of their own young adulthood in the novels. The study employed both qualitative and quantitative data collection methods through questionnaires completed by 30 Malaysian YAs, semi-structured qualitative interviews with a sub-group of six participants, and their journal reflections. Using reader-response literary theory as the guiding framework, the data were analysed quantitatively through descriptive statistical analyses, and qualitatively through inductive thematic analysis, in order to examine the extent to which Malaysian YAs could identify with the main characters, themes, issues, or events in the novels and determine the relevance of the novels to their lives. The findings showed that the participants identified with the characters’ conflict between being true to one’s self and conforming to societal and gender expectations. The themes of standing up for one’s beliefs and right to education, combating social inequities, and family relationships were also relevant aspects that surfaced in responses towards the novels. This study provides recommendations for the selection of literary texts for the English language classroom that connect to the developmental phase of young adults and allow learners to see themselves reflected in what they read.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".