Claiming Their Voice: Sociolinguistic Factors Affecting Immigrant Workers’ Ability to Speak Up
Bibliographic record
Abstract
Immigrants’ multiple identities are sources of contention as they strive in the English-speaking workplace, where they need to meet job demands and demands from employers who expect them to conform to the culture of the management (Harper, Peirce, & Burnaby, 1996; Jacobson, 2003; Katz, 2000). With California having a significant immigrant worker population, this study investigated how many of these workers navigate multiple identity and cultural issues while attempting to use their learned English to claim their voice. In an adult ESL classroom, first qualitative data were collected from students’ responses about a workplace scenario. Then, 3 individuals from the class were chosen for in-depth interviews to determine factors that contribute to or hinder their ability to stake their claim in the workplace and speak up for themselves. The study results showed that several sociolinguistic factors influence whether or not workers chose to speak up and that these factors are as pertinent as workers’ linguistic proficiency and the types of employers and coworkers they have. The authors discuss pedagogical implications with the goal of empowering immigrants to claim their voice at the workplace.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".