âOnce and Futureâ Directions in Language Teaching and Life: An Interview with Marianne Celce-Murcia
Bibliographic record
Abstract
When professor Celce-Murcia retired in 2002, she was interviewed by IAL; back then she talked a little about her early studies and educational background, and about changes she had witnessed not only in the field of applied linguistics and teaching English to speakers of other languages (TESOL) but also within our department, with our newly created undergraduate minor in TESL -Teaching English as a Second Language.In addition, she outlined two of her most impressive works: The Grammar Book (co-authored with Diane Larsen-Freeman) and Teaching Pronunciation (co-authored with Janet Goodwin and Donna Brinton).But contrary to what one would expect, retiring for her did not mean going home victoriously at last, to find the long-deserved and inviting arms of Morpheus; after all, she had already greatly contributed to society with an impressive list of accomplishments and hallmark books that are still hailed as the best of their kind.Instead, it was just the beginning of a new set of challenges and projects.In this interview, Marianne addresses some of the projects and hardships that awaited her after her retirement, along with her unexpected appointment to serve as dean of English programs at the American University of Armenia, and the creation and co-editing of an innovative discourse-based ESL textbook series.
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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.014 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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".