Bibliographic record
Abstract
My first experience working on an English-as-a-second-Ianguage (ESL) syllabus was a few years ago when I was teaching at a private English conversation school in Japan.I was asked to lead a small group of teachers in developing syllabuses for 15 levels of classes that included classes for children, teenagers, and adults.Relying mainly on instinct, we used models we found in the front of textbooks and inserted our own lists of grammar points, functions, and vocabulary; a couple of months later, however, the teachers were either finding the new syllabuses difficult to use or had stopped using them completely.My first syllabus project was a flop.Since then I have had experience with and been a keen observer of ESL syllabus development and use in different teaching situations overseas and in Canada.Consistent with my own earlier experience, I have noticed that (a) the various participants in an ESL instructional setting seem to recognize in a general sense the value of a syllabus (i.e., students and teachers want the kind of guidance that a syllabus can provide; administrators appreciate the accountability that a syllabus brings and its potential for marketing); and (b) there is often a lack of understanding among practitioners of what a syllabus is (or could be), which in tum has a negative impact on both syllabus development and use.My interest in ESL syllabus development has also led me to notice a relevant gap in the second language (L2.) education literature.The syllabus was a source of interest here particularly in the 1970s (e.g., work on functional and notional syllabuses [Van Ek & Alexander, 1975;Wilkins, 1976] and the 1980s (e.g., introduction of the task-based syllabus [Nunan, 1989;Prabhu, 1981] and several books on L2 syllabus development [Nunan, 1988;White, 1989].This work contributed a great deal to our understanding of the nature and role of the L2 syllabus.However, in the last 10 years, as the broader field of L2 education has continued to evolve, it seems that less attention has been given to the syllabus in the literature.Given the crucial role a syllabus can play (and, many would agree, should play) in an ESL program, I think it is necessary to redirect attention to and reexamine our understanding of the L2 syllabus.This article is an attempt to do so and is structured around three questions that were derived from the situations described above: 1.What is the L2 syllabus?
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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".