Producing the patchwork: the hidden work of mothers in organizing child care
Bibliographic record
Abstract
This article aims to explore how child care is organized in families, documenting how mothers produce their individual child care solution, or “patchwork”, within the context of Canada’s underfunded and fragmented child care system. In a sample of 109 mothers from Alberta, Canada, where child care is conceptualized as primarily a private family responsibility, we use an ecocultural theoretical framework and a gender lens to 1) identify the constraints that influenced what kinds of child care mothers used, 2) explore the organization of day-to-day child care arrangements, and 3) explicate the accommodations and flexibility required to sustain the family routine. We show that in addition to previously recognized categories of child care—formal, informal, and mixed—families also used multiple informal and parent-plus (i.e., parental plus non-parental) child care. The procurement and management of child care—particularly when multiple care providers were involved—was gendered, often invisible, and required substantial accommodations and flexibility by mothers. We propose a day-care plus policy model of child care, where formal arrangements are supplemented as required. This policy model could help families avoid the complex scenarios we conceptualize as chaotic flexibility and assist families in achieving sustainable flexibility in the organization of care.
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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 | 0.004 | 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 | 0.016 | 0.018 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".