Bibliographic record
Abstract
[First part only] Dear Readers, While this issue is a bit slimmer than usual, it contains an informative set of articles on a variety of currently relevant topics: Creativity-nurturing practices in ELT (Iran), Reading circles (Mali), Learner agency (Indonesia), Summer English camps (Canada) and Code-switching & translanguaging (Japan). Readers might have noticed that the On the Internet article for this issue was pre-released so that it would be available for reference to teachers in schools that start their term in or around September. We thank Ilka Koska & Rachel Toncelli for offering “Exploring Applications of ChatGPT to English Language Teaching: Opportunities, Challenges, and Recommendations” to the TESL-EJ audience. A Retrospective. This issue offers a new feature — a reprint of a published article. As Scott Windeatt states in his foreword: “Because of the speed with which technology develops and becomes obsolete, we tend to assume that literature on technology loses relevance at the same pace.” The article included with this issue proves this assumption wrong. Originally published in the CAELL Journal in 1990 is strikingly relevant to today’s pedagogy. The article, by the late Vance Stevens who was on “On the Internet” editor here for 19 years, is one of a collection of articles that will eventually be published as a book of his writings, edited by Maggie Sokolik, the founder of TESL-EJ and its official publisher. We are pleased that the author of one of Vance’s references, Scott Windeatt of Newcastle University, UK., has written a short preface to help situate readers to the article. Passages. With great regret, we announce that Nicholas (Nicky) Bremner has decided to take a vacation from his role of gatekeeper of new submissions. With great trepidation, Tom Robb, the editor-in-chief, resumes this role until Nicky manages to find time for us again. Thanks to recent reviewers. Continuing our new policy, we thank the reviewers of all submissions that had reached a decision since our last issue – regardless of whether the paper was accepted or declined. Thanks go to: Adele Connor, Anita Lie, Anna Riana Suryanti, Arezou Maleki, Atipat Boonmoh, Azad Ehamadi, Azran Azmee Kafia, Behnam Aghayani, Behrang Mohammad-Salehi, Chingling Wo, Colin William Campbell, Dale Brown, David Imamyartha, Dodi Mulyadi, Elise Nicole Brittain, Eric Hagley, Fatemeh Ranjbaran, Ghasem Modarresi, Graeme Couper, Ha Hoang, Humairah Fauziah, Jardel Coutinho dos Santos, Jieun Kim, Juland Salayo, Kathryn Shelley Price-Jones, Kuang Li, Laila Noor, Larry Xethakis, Loc Tan Nguyen, M. Obaidul Hamid, Marzieh Bagherkazemi, Matthew Nall, Mehrdad Mohajerpour, Meliha R Simsek, Michael Mauricio, Mohammad Kazemian, Musa Nushi, Nataly Karikian, Olive Nabukeera, Onur Ergunay, Paul Mathieson, Peter Ilic, Piyapong Laosrirattanachai, Ranson Lege, Reman Sabah Meena, Reza Adara, Ryan Spring, Suyansah Bin Swanto and Wen Sun. Best, Thomas N. Robb, Ph.D. For the editorial team
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 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; both teacher heads agree on what is shown here.
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".