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The Ethics of TESOL a quarter century on

2018· article· en· W2906286156 on OpenAlexaboutno aff
Alan Williams

Bibliographic record

VenueTESOL in Context · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Quarter (Canadian coin)CurriculumProject commissioningSociologyGlobalizationPublishingPedagogyEngineering ethicsProfessional developmentSociocultural evolutionWork (physics)Social sciencePublic relationsPolitical scienceEngineeringLawHistory

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.874
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.045
GPT teacher head0.285
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

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