“Non‐coercive Rearrangements”: Theorizing Desire in <scp>TESOL</scp>
Bibliographic record
Abstract
In this article, the authors argue that at the center of every English language learning moment lies desire: desire for the language; for the identities that English represents; for capital, power, and images that are associated with English; for what is believed to lie beyond the doors that English unlocks. However, despite its centrality within TESOL practice, the construct of desire has been largely undertheorized by English language educators. The authors propose (1) that educators in the TESOL field would benefit from a greater recognition of desire as situated and co‐constructed, acknowledging that our desires are not solely our own but are intersubjectively constituted and shaped by our social, historical, political, institutional, and economic contexts; (2) that the difference between conscious and unconscious desire is significant in language learning; and (3) that whereas desire can be manipulated in exploitative or unethical ways, it can also, given the right circumstances, serve as a tool for compassionate and liberatory pedagogy. This article explores the interconnectedness of desire with motivation and investment, the commodification of English, akogare desire, racial identities, globalizing forces, colonialism, and communicative language teaching. The authors propose a revisioning of TESOL that recognizes the centrality of desire in the acquisition of English.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.040 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".