Some Recommendations for Integrating Literature into EFL/ESL Classrooms
Bibliographic record
Abstract
Many EFL/ESL learners find English language classrooms boring, partly because of the fact that learners are engaged in those activities they consider unrelated to the requirements of out-of-class communication in the L2. One solution offered to this problem is to introduce literature and literary texts into language classrooms. Many researchers support the proposal that literature needs to be incorporated into language teaching curricula, both for children and adults. However, researchers and ELT practitioners do not agree as to what are the most effective procedures for integrating literature into language classrooms. It is the purpose of the present paper to offer some recommendations that would help language teachers maximize the efficacy of their literary materials. Ten recommendations will be proposed with a specific focus on short stories and novels along with the rationales as to why it is thought that the recommendations would be helpful for literature-based language classrooms.
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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 | 0.040 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".