Workplace Language Teaching and the Intercultural Construction of Ideologies of Competence
Bibliographic record
Abstract
Drawing on data from a larger ethnographic study of workplace literacy programs serving immigrants in California, this article describes workplace literacy classes at Cableco, a cable manufacturing plant serving the booming electronics industry in California's Silicon Valley. Language classes began at the factory in the mid-1990s to help immigrant employees gain English skills that managers perceived as crucial to promoting smooth work flow and good business. Blending ethnographic, critical discourse analytic and interactional sociolinguistic approaches, the author illustrates how a language lesson called ‘Who Gets Recognition in the United States' reveals underlying assumptions about gender, power, group allegiance, and politeness. These ideologically grounded assumptions were embedded in the lesson as well as in broader workplace language practices shaping managers' and employees' diverging conceptions of competence and their perceptions of one another. The data - a workplace literacy lesson, employees' responses to it, and an interview with the manufacturing manager - demonstrate some of the dangers of conceiving of language as a discrete workplace skill. They also illustrate how discursive practices can engender unequal institutional power relations while simultaneously providing a space, albeit a very small one, for workers to contest managers’ attempts to reshape their identities through language practice.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".