Coalition-Building and the Fight for Universal Child Care in Ontario, Canada, 1981-2022
Bibliographic record
Abstract
This thesis constructed a chronological account of the advocacy work of the Ontario Coalition for Better Child Care (OCBCC) from 1981 until 2022, evaluating how effectively the OCBCC advanced an inclusive vision of child care that served all families and child care workers. This account of the OCBCC was based on a descriptive analysis of news articles, Hansard transcripts, organizational documents, and interviews with former OCBCC executive, OFL and CUPE staffers, child care researchers, and Registered Early Childhood Educators (RECEs). I also used Critical Discourse Analysis (CDA) to identify discourses used by government to justify child care reform and the OCBCC in its campaigning for a public child care system. I found that although the OCBCC campaigned for reforms that benefitted low-income racialized families, including campaigning for expanding child care subsidies, the OCBCC did not engage in more explicit anti-racist campaigning to demand culturally appropriate and anti-racist child care. Similarly, although the OCBCC campaigned to improve the wages and working conditions of child care workers as a highly feminized and racially diverse profession, the OCBCC initially opposed supporting child care’s integration with the education system in the 1980s and more disruptive forms of direct action in support of child care workers. I discovered that even as neoliberal reform, including austerity, deregulation, and marketization, undermined the actualization of the OCBCC’s child care objectives, neoliberalism and its ongoing threat also had a conservatizing effect on the OCBCC and its affiliates. This occasionally undermined the coalition’s ability to advance an inclusive child care system. The defunding of women’s advocacy groups by the federal government left the OCBCC with fewer resources to support the equitable inclusion of marginalized workers and parents within the OCBCC’s decision-making executive bodies. Austerity and its threat also conditioned the OCBCC’s non-profit child care operator affiliates to oppose reform beneficial to child care workers because of concerns over rising labor costs and lost revenue in the absence of sufficient direct operational funding from scarcity-minded governments.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.025 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".