Transgressive vs conformative: immigrant women learning at contingent work
Bibliographic record
Abstract
Purpose The paper seeks to explore workers' learning in relation to the racialized and gendered organization of contingent work. Design/methodology/approach This paper is informed by Marxist theorization of labour power and learning. It draws on the interview data of 24 highly educated immigrant women from the research project “Skilled In Vulnerability: Work‐related Learning Amongst Contingent Workers”. Findings The two types of learning that we have identified include conformative and transgressive learning. Conformative learning refers to the learning that workers engage in to conform to the workplace expectations. The workers are not rewarded or acknowledged for their learning endeavors, and their efforts end up being appropriated by employers to maximize surplus value. Transgressive learning refers to the learning initiatives undertaken by workers to challenge and transcend the depressing work environment and life conditions. Research limitations/implications This research was not able to capture what conditions facilitate transgressive learning versus conformative learning. Practical implications The study first calls on the state to deal with the systemic negation of professional immigrants' previous skills and learning experiences. Second, it encourages employers to provide appropriate training and reward the workers for their learning endeavor. Third, drawing on the research findings, work educators could help foster the forms of learning that are conducive to workers' better control of work and life. The authors also suggest that to optimize their work and life opportunities in general, immigrant women workers need to come together, exchange information and knowledge, learn from each other, and form collective actions amongst themselves. Originality/value This paper contributes to the literature on workplace learning from a learning perspective. It is one of the first endeavors to look at how highly educated immigrant women learn to exercise agency to challenge contingent work structures.
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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 | 0.007 |
| 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 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".