TESOL and Policy Enactments: Perspectives From Practice
Bibliographic record
Abstract
Prior research in the area of language policy and planning (LPP) has been focused primarily on macro decision‐making and the impact of national, local, and institutional policies in educational settings. Only recently have scholars begun examining the everyday contexts in which policies are interpreted and negotiated in ways that reflect local constraints and possibilities. The redirection of inquiry toward situated policy enactments in TESOL is the central theme of this special issue and the introductory article. In this article we address and expand on several key themes that arise from and unify the various contributions to the issue: (a) the enhanced status and implications of locality in policy research, (b) practitioner agency and the ethical concerns involved, (c) the globalization of particularistic agendas (i.e., neo‐liberalism) and their impact on nation‐state identities and policy enactments.
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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 | 0.075 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.020 | 0.113 |
| Scholarly communication | 0.038 | 0.030 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 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".