Bibliographic record
Abstract
Teaching English to Speakers of Other Languages (TESOL) is one of the largest educational enterprises in the world. Tens of thousands of teachers—both native and non-native speakers of English—are engaged in TESOL across the world. This large population of teachers depends heavily on academic researchers for developing their knowledge base. Although it is evident that teachers who engage in classroom research are more aware of their practices and better able to facilitate student learning, teacher-research is a minority activity in the field of TESOL. In this article, I briefly discuss TESOL practitioners’ conceptions of research. Then, I focus on a dichotomous relationship between qualitative and quantitative approaches to research, and review some contemporary orientations to TESOL research. I conclude the article with a recommendation that TESOL practitioners engage in action research for their professional development as well as their students’ increased learning of the target language.
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 | 0.040 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.011 | 0.087 |
| Scholarly communication | 0.026 | 0.019 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.008 | 0.010 |
| 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".