Language tests and neoliberalism in “global human resource” development: A case of Japanese Universities
Bibliographic record
Abstract
Abstract This study looks into the increasing emphasis on the use of language tests for global workplace preparation in Japan. It presents particular usages of English language tests in higher education curricula designed to foster “Global Human Resources” (GHRs), a special global workforce with high levels of English proficiency deemed necessary by the Japanese government. Focusing on a government-initiated five-year funding program, “The Project for Promotion of Global Human Resources”, government documents and the project planning sheets of 11 universities are analyzed to trace how language tests act as a form of governmentality (Foucault. 2007 [1977]. Security, territory, population: Lectures at the Collège de France, 1977–1978 (Trans. by Graham Burchell). New York: Palgrave Macmillan) to maximize the number of GHRs. I describe how language tests are used to portray a reality about the lack of English proficiency among Japanese youth, how they work as a powerful accountability measure for universities, and how these tests are incorporated into language education curricula with the goal of increasing students’ language capital. Three functions of language tests are then identified in the universities’ proposed curricula: motivating, categorizing, and prioritizing through testing. These governing techniques represent how language tests can work to promote neoliberal forms of international education that instrumentalize language learning, stimulate inequitable competition, and (un)reward certain global subjectivities.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".