Unregulated, Unknown and Highly Necessary: Grey Market Nannies
Bibliographic record
Abstract
Canada’s robust yet overburdened public childcare drives reliance on a “grey market” of nannies and private caregivers, often mediated by digital gig platforms. This paper analyzes over 6,000 nanny profiles scraped from CanadianNanny.ca in the Greater Toronto–Hamilton area to investigate how platform-enabled self-presentation reproduces inequality in care labour. Using natural language processing (sentiment, n-gram, and LDA topic modeling), we find a stratified market where self-presentation diverges by wage tier. Lower-wage caregivers (under $20/hour) disproportionately frame themselves as "generalists," bundling childcare with extensive domestic labour (housekeeping, meals). In contrast, higher-wage caregivers (over $20/hour) emphasize professional credentials, specialized skills (e.g., newborn care), and formal training. These results show that digital platforms actively structure care labour, reinforcing racialized and gendered hierarchies that devalue generalist labour while conditionally valorizing professionalized care. We argue that Canada’s fragmented system entrenches this dual process, the devaluation of reproductive labour for the many and conditional valorization for the few, underscoring the urgent need for policy to regulate platform work and ensure equitable recognition and protection for all care workers.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".