Don't Leave Home Without Theml
Bibliographic record
Abstract
Anyone involved in ESLIEFL teacher-training is aware that a signifi-cant number of graduates of these programmes will take up positions teaching English abroad. What is more, it is extremely common for teachers to receive appointments abroad, and for them to be given little or no information about exactly who or what they will be teaching, and under what circumstances, before they actually arrive at their destination. What is more, it is not uncommon for teachers to be hired to teach EFL, but to learn on their arrival, that they are also expected to train teachers of EFL. This is a reality that has existed for many years, and has not shown much sign of improving. The purpose of this Symposium was to provide some assistance to teachers who find themselves in exactly this situation: they know that they are going to teach English abroad, but know very little else about their responsibilities. Despite the lack of information, they want to take with them the most useful material that they can, particularly given that books, photocopying facilities, etc., will likely be limited where they are teach-ing. They are also acutely aware that they will be limited in the amount that they will be able to take. A panel of six experienced teachers/teacher-trainers was assembled to participate in this Symposium, and they were asked to prepare a list of the ten items that they would take with them if they had accepted a position to teach English abroad, and all they knew was that they were going to be teaching adults. Each panelist submitted their annotated list of ten items, and presented their reasons for choosing this list. 2 As will become evident below, the lists and the panelists ' reasons for inclusion or exclusion of different items varied greatly. However, this variation itself is very useful, and will, it is hoped, provide teachers with food for thought as well as providing a very valuable annotated list of some sixty highly regarded and important books. Patrick Allen, Ontario Institute for Studies in Education In compiling my list, I have concentrated on three types of material: (a) books which provide background information on grammar, vocabulary and discourse; (b) 'focused input ' designed to encourage error correction and consciousness-raising in the classroom; (c) materials which provide
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.603 | 0.419 |
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".