Bibliographic record
Abstract
This chapter discusses how dynamic processes of gendering, racialization, and precarization make diverse people into personal support workers who lack security at the labor market and intimate levels. Enduring gendered inequalities that relegate more women than men to unpaid domestic work serve to structure and justify the concentration of women in this paid domestic work and its devaluation. What immigrant women from professional and working-class backgrounds had in common that shaped their eventual location in personal support was the marginal place of their nation of origin in the global economy vis-à-vis the United States, Canada, and by extension Britain. Gendered and racialized migration shaped the location of immigrant workers in North America, but their entry into personal support had as much to do with dynamics in the local labor markets of Toronto and Los Angeles, namely the intersection of racialization, gendering, ageism, and precarious employment, supported by the state. Social networks certainly opened up jobs to immigrant workers with few other options, but these jobs were precarious.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".