Bibliographic record
Abstract
In this paper I report on a research project designed to address the question of how the policy of continuous enrolment has been working in practice in the AMEP (Adult Migrant English Program), the national English language program offered to newly-arrived migrants to Australia. Managers, teachers and learners from around Australia were interviewed individually or in focus groups to ascertain their views on the policy and its management. The literature on continuous enrolment has focused almost exclusively on adult education in North America, and has generally found little positive support for the policy among teachers. The results of this study indicate that the potential benefits to students in the context of the AMEP may outweigh the considerable disruption to classes it causes. In two of the three participating centres, the students were overall very positive about starting class immediately, and many teachers also appreciated these benefits for students and were developing strategies to minimize the negative effects. Similarly, while the managers generally recognized the organizational and pedagogical headaches that the policy caused, they appreciated the flexibility it gave them to open and manage classes according to local conditions. I argue that these more sympathetic views are a product of the unique context and history of the AMEP as a nationally-supported on-arrivals program, but that positive measures are nevertheless necessary in order to address the issues caused by continuous enrolment.
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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".