Bibliographic record
Abstract
Discussion of ethical considerations in Australian TESOL began 25 years ago, with arguments about the need for TESOL professionals to be aware of the potentially harmful consequences of their work, the loss of first language proficiency, and even the loss of languages themselves (Williams, 1992, 1995). The intervening quarter of a century has seen sweeping changes to the context in which TESOL professionals work and developments in our professional knowledge about the processes and consequences of TESOL professional practice (Canagarajah, 1999; Phillipson, 1992, 2013). In this paper developments in the sociocultural context of TESOL, the general education context and the TESOL professional context are explored. This article revises the arguments about ethical directions in TESOL presented a quarter century ago to take account of these changes. Guiding principles for individuals and professional bodies are identified. It is argued that our role is to sensitively help our learners to explore the potential consequences of the learning of English, and for professional bodies to take an active role in advocacy given the impact of globalization processes, more centralized curriculum and assessment frameworks, and the relatively reduced capacity of individual teachers to influence the institutions that employ them.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.042 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".