Bibliographic record
Abstract
Many of us have just returned from an invigorating week in Vancouver at the TESOL 2000: Navigating the New Millenium.At conferences such as these participants seek to advance our profession through sharing research and teaching experiences as well as through service work.Of particular interest to one of the editors was not only the journal editors' sessions organized by the editors of the TESOL Quarterly, but also the TESL Canada sessions on National Recognition Standards.At the editors' sessions one insight became clear: the TESL Canada Journal, along with the majority of the 51 other refereed journals in our field, is a mentoring journal.That is, authors can receive advice and assistance in preparing their manuscripts for publication-assistance from blind reviewers and from editors.It is usual for authors to write several drafts before getting published.The National Recognition Standards project of TESL Canada is well in progress.Its purpose is "to promote excellence in teaching ESL across Canada."TESL Canada is taking its rightful place in leading the professionalization of all classroom teachers.We would like to encourage the committee, and the membership, to specify standards not only for adult ESL teachers, but also for teachers of K-12 ESL students.Although education is politically a provincial responsibility, TESL Canada needs to give educational and professional leadership for teachers of all ESL students.TESL Canada is also preparing institutional accreditation guidelines.Three sets of guidelines may be needed: (a) for institutions of ESL teacher education, (b) for institutions of adult ESL, and (c) for K-12 schools with ESL students.
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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.026 |
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".