The social construction of skill : skill and working knowledge of garment workers in a Vancouver clothing factory
Bibliographic record
Abstract
An examination of the working knowledge of female garment workers provides the empirical context for the analysis of gendered and racialized notions of skill in a Vancouver garment factory. This thesis problematizes how the labour of garment workers is socially constructed as being of low-value and lowskill, both inside and outside the factory. The social construction of skill on the shopfloor is examined through ethnographic observation of garment construction on the shopfloor and semi-structured interviews with floor-workers, management and union leaders conducted over eight months serve to describe My findings indicate that in spite of managerial strategies that serve to create categories of unskilled work and workers alike, operators act knowledgably and competently on the job. But the social construction of skill, permeated with gendered, racialized and class-based attributions, tends to make invisible the working knowledge of operators. In a contradictory way, being considered skilled turns into a disadvantage for those workers who are expected to perform even more efficiently without increased financial reward. Thus being recognized as more skilled becomes problematic for garment workers. As this study shows skill is a contradictory social construct defined by ideological and political interests. Definitions of skill are as much a reflection of a contradictory labour process as an expression of differential power and privilege. The preservation of power and privilege is in fact the subtext for managerial definitions of skill that render female garment workers as unskilled. This finding points to the need for a critical reflection on the utility of the concept of skill and questions whether skill is a viable concept for explaining and reflecting on the working knowledge of operators, and suggests that notions of tacit skill and working knowledge might be more fruitful. Shortly after my fieldwork ended the plant observed in this study ceased production and moved its operations to a Free Trade Zone in Central America. The same social processes that allow the construction of garment workers as "unskilled" on the Vancouver shopfloor produce even cheaper "unskilled" labour globally.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".