A Field of Exciting and Diversified Opportunities: An Interview with Donna Brinton
Bibliographic record
Abstract
A Field of Exciting and Diversified Opportunities: An Inter- view with Donna Brinton Innhwa Park University of California, Los Angeles Donna M. Brinton retired in November 2006 from her position in the Depart- ment of Applied Linguistics & TESL at the University of California, Los Angeles (UCLA) to devote more time to her work in international teacher development. She currently serves as Senior Lecturer in TESOL at the Rossier School of Education, University of Southern California. Looking back at her invaluable contribution dur- ing the 27 years of service at UCLA, we come to appreciate the breadth and depth of her career. She served in the capacities of Academic Coordinator of the UCLA ESL Service Courses, Lecturer in Applied Linguistics, and Associate Director of UCLA’s Center for World Languages. She is the co-author and co-editor of several professional texts including Content-Based Second Language Instruction, The Content-Based Classroom, Teaching Pronunciation, New Ways in Content-Based Instruction, New Ways in ESP, and Heritage Language Education: A New Field Emerging. She has also co-authored several English language textbooks, on-line content-based lessons for EFL learners 1 , and numerous articles in refereed journals and edited texts. For twelve years she also co-edited The CATESOL Journal. Adding to this impressive list of professional achievements, she has con- ducted short-term international teacher training and program evaluation in Thai- land, Vietnam, Myanmar, Korea, Japan, Taiwan, New Zealand, China, Canada, Mexico, Guatemala, Colombia, Bolivia, Argentina, Chile, Paraguay, Brazil, Israel, Senegal, Mali, South Africa, Mozambique, Madagascar, Mauritius, Uzbekistan, Turkmenistan, Syria, Lebanon, Tunisia, Morocco, Libya, and Curacao. Finally, with co-authors Marianne Celce-Murcia and Janet Goodwin, she recently completed the 2 nd edition of Teaching Pronunciation and is currently working on the second edition of The Structure of Modern English (with Laurel J. Brinton). In this interview, Donna reflects on her years at UCLA, her involvement with different projects such as teacher training and program evaluation, and her experience in material development. She also shares her thoughts on publishing and attending academic conferences. As someone who has held numerous sig- nificant roles such as lecturer, teacher trainer, educational consultant, and author, Donna gives insightful advice to current graduate students in the field of Applied Linguistics and TESL. Innhwa: I feel very fortunate to have had the opportunity to learn from you while you were teaching at UCLA, and to be able to have this continuing relationship with you. Thank you very much for doing this interview with us. Issues in Applied Linguistics © 2009, Regents of the University of California ISSN 1050-4273 Vol. 17 No. 1, 51-65
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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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.045 | 0.028 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.011 | 0.028 |
| Insufficient payload (model declined to judge) | 0.006 | 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".