Life on the line : Indigenous women cannery workers' experiences of precarious work
Bibliographic record
Abstract
This dissertation examines the experiences of Indigenous women engaged in precarious and seasonal salmon cannery work. The dissertation argues that to grasp the nature of the women's work, which is exceedingly precarious, it is necessary to consider how it is shaped by a host of social, political, environmental and economic forces. In particular, the dissertation illustrates how provincial and Canadian neoliberal policies that developed during the past few decades have amplified the vulnerable status of Indigenous women cannery workers. Neoliberal discourses of active (worthy) and passive (unworthy) citizens embedded in social policies powerfully shape qualification requirements to programs such as Employment Insurance and Income Assistance while individualizing social inequalities experienced by Indigenous women. The dissertation employs both decolonizing and feminist methodologies to examine the everyday experiences of Indigenous women and to map out the social relations that shape their experience as precarious workers. Overall the dissertation contributes to making Indigenous women worker's lives more visible, to showing their significance in the salmon canning industry, to highlighting how their precarious labour undermines their well being and that of their families, and to demonstrating their resilience in the face of major obstacles.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.019 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".