Does Reading Literary Texts Have Any Impact on EFL Learners’ Vocabulary Retention?
Bibliographic record
Abstract
Abstract: This study seeks to find out whether EFL learners’ exposure to literary texts is in any way different from their exposure to nonliterary texts with respect to their ability to deal with related vocabulary items. Furthermore, the present study also sets out to examine whether EFL learners perform differently on reading comprehension tests derived from literary texts compared with those derived from nonliterary ones. The analysis of the data brought to light the fact that exposure to a plethora of literary texts does not imperatively bring any significant gain in the comprehension of literary or nonliterary texts. Even so, it is concluded that exposure to vocabulary items in literary texts may, in point of fact, help EFL learners to build up solid vocabulary knowledge. Key words: Extensive Reading; Literature; Reading Comprehension; Vocabulary Acquisition Resume: Cette etude cherche a decouvrir s’il y aurait des differences aux niveaux des resultats en comparant avec l’exposition des etudiants d'EFL aux textes litteraires et des etudiants d’EFL qui sont exposes aux textes non litteraires en ce qui concerne de leur capacite de traiter les articles relatifs de vocabulaire. En outre, la presente etude egalement mise a examiner si les etudiants d'EFL executent differemment sur des essais de comprehension de lecture derives des textes litteraires compares a ceux derives de les non litteraire. L'analyse des donnees a mis en evidence le fait que l'exposition a une plethore de textes litteraires n'apporte imperativement aucun gain significatif dans la comprehension des textes litteraires ou non litteraire. Neanmoins, on le conclut que l'exposition aux articles de vocabulaire en textes litteraires peut, en effet, aider des etudiants d'EFL a accumuler la connaissance solide de vocabulaire. Mots cles: Lecture etendue; Litterature; Comprehension de lecture; Acquisition de vocabulaire
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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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".