Bibliographic record
Abstract
That the title of this chapter is ESL in adult education and not one of the possible alternatives such as teaching English to adults or ESL for adults is significant in two ways. The title situates the chapterwithin the contested dichotomy of ESL versus EFL. Further, it frames this ESL instruction within adult education, that is, those systems established in English-dominant countries such as Australia, Canada, Great Britain, New Zealand, and the United States to provide high school subjects to adults who did not graduate from high school or to provide general interest subjects such as conversational Spanish or computer literacy. In other words, this framing differentiates between non-immigrant adults learning English (such an in intensive English programs) and immigrant/refugee adults learning English. Therefore, the focus of this chapter is on the teaching of English to adult immigrants and refugees. However, the chapter focuses on issues of particular interest to this population-issues of curriculum, program evaluation, particular learner characteristics, and assessment of learning. Althoughmany other topics, such as reading strategies, learner identity, and task-based learning, are topics relevant to ESL in adult education, these topics will be dealt with in other chapters of this Handbook. The choices have been made based on providing readers with an overarching framework of ESL in adult education in English-dominant countries.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".