Bibliographic record
Abstract
Presenting comprehensive research conducted with learners and educators in a range of settings, this volume showcases self-reflection as a powerful tool to enhance student learning. The text builds on empirical insights to illustrate how language professionals can foster critical self-reflection amongst learners of English as an additional language. This text uses ecologically sensitive practitioner research that addresses issues of both practical and pedagogical significance in the fields of TESOL, language teaching and learning, and teacher education. By synthesizing interdisciplinary research and theory, chapters show how various types of self-reflection—including guided and non-guided; group and individual forms; and written, oral, and technology-mediated reflection—can promote autonomous, self-regulated learning amongst students at various levels. Whilst offering readers a strong grounding in the theoretical and empirical knowledge that supports self-reflection, the volume gives constant attention is given to praxis, with a focus on effective pedagogical strategies and tools needed to implement, encourage, and evaluate critical learner reflection in readers’ own teaching or research. This volume will be a critical resource for language-teaching professionals interested in critical learner reflection, including in-service, pre-service, and teacher educators in the field of TESOL. Scholars and researchers in the fields of applied linguistics and language education more broadly will find this volume valuable.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".