Language‐in‐Education Policies in Japan Versus Transnational Workers’ Voices: Two Faces of Neoliberal Communication Competence
Bibliographic record
Abstract
Foreign language education primarily aims to cultivate learners’ competence to communicate in an additional language. However, the meaning of communication competence is not entirely transparent, especially given the current neoliberal valorization of communication in the knowledge economy. The meaning of communication can be scrutinized in two contradictory trends observed in language education: the exclusive focus on teaching English as a global language, signifying a homogenizing trend, and increased scholarly attention to the heterogeneity of linguistic forms and practices. This article examines how communication competence is differentially understood by policymakers and corporate workers in Japan. The authors examine a government report that evaluated the attainment of educational goals for coping with globalization and contrasting it with interview data drawn from another study on the communicative experiences of Japanese transnational workers in Asia. Political discourse analysis and content analysis reveal the paradoxical nature of what can be called neoliberal communication competence, which on the one hand conflates global communication with use of the four measurable skills in English to transmit information and, on the other hand, challenges linguistic norms, foregrounding plurilingualism and co‐constructed interactional competence. Transformation of policies and pedagogies can be pursued by appropriating neoliberal communication competence for achieving broader educational goals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".