TESOL initial teacher training and TESOL curriculum goals: \nmaking the connection \n
Bibliographic record
Abstract
APEC countries represent , in TESOL terms, two very different groups. Firstly there are the English L1 countries (US, Canada, Australia, NZ) where the English proficiency of the majority can be taken for granted and where English needs to be taught only as a second language or additional language to people arriving in the countries from overseas. In the second group, the majority of APEC countries, English is a foreign language, and if a government feels it to be an important aspect of the wider curriculum, systems need to be established to provide English teaching to all pupils in all schools. These are very different TESOL environments and what is appropriate and helpful in one, is not necessarily of relevance to the other. The paper that follows focuses on the EFL context typical of the majority of APEC members, although at times it draws from literature deriving from the English native speaker members also. \n \n
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.177 | 0.058 |
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".