How Teachers Adapt Nine Kinds of Literary Language for Second-Language Learners
Bibliographic record
Abstract
English literature is taught around the world. Most teachers of English in China are non-native English users. Most adapt literary texts for their second-language classrooms. Little research explores their processes of modifying texts. This study analysed data from 202 in-service and intending teachers, over four years. Teachers were asked first to adapt Jebb’s translation of Antigone, and then to adapt a second play text, either a Shakespeare or a 20th century play. The time taken to adapt Antigone was assessed, yielding a time estimate for adapting a full-length play text, as well as per page of literary language. Likert-scale survey data was taken for 9 different kinds of difficult language found in literary texts. Results indicated that NNESTs require about 40 minutes per page when adapting modern literary language. The find retaining poetic qualities while reducing text length, that is, moving between lexicogrammatical and discourse levels of the text, the greatest challenge in adapting literary language. They find texts with contemporary lexis and grammar are easier, where classical and historical references, subplots and details are difficult to handle. They find the task satisfying, pleasurable and interesting.
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.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".