Invisible Work and Hidden Labour in Ontario’s Public Education: A Decolonizing Institutional Ethnography of Mothering and Teachers’ Work
Bibliographic record
Abstract
This study explores how one institutional text, Ontario’s Parental Engagement Policy (2010), enters into and coordinates the work of mothers and teachers in publicly funded education. An institutional ethnography was conducted based on in-depth interviews with ten mothers and ten teachers of students in Grades 4 to 6 in the Greater Toronto Area (GTA) comprising of the municipalities of Toronto, York, Durham, Peel and Halton. The data was analyzed using Smith’s framework that keeps the institutional ruling relations in view. The findings indicated that Ontario’s Education Act and Parental Engagement Policy operationalized governance through a network of subordinate texts and textually mediated mothering and educational discourse routinely activated through the text-reader dialogic of everyday work. While the efforts of the Ontario policy appeared to invite parents to connect with their children’s teachers to understand schooling and support their children, the data highlighted how the teacher union, the Elementary Teachers’ Federation of Ontario’s professional judgement and professional conduct guidelines subjugate parental engagement by asserting teachers’ expertise over these attempts and efforts. I argue that due to the racist Standard North American Family (SNAF) ideology of Ontario’s Education Act and policy, as well as the power relations of ETFO, mothering work -a valuable contributor to students’ learning outcomes -is largely marginalized and undervalued in GTA schools especially when done by women caught in the intersections of raced-class, immigration trajectories and linguistic hegemonies of socially dominant languages within the North American schooling context. I conclude that ETFO and the Government of Ontario operate as isomorphic organizations with similar relations of ruling that create invisible barriers for parental engagement as well as the advocacy practices of equity-minded, anti-racist teachers. Social change recommendations include community-led, multilingual, outcomes-driven parent advocacy collaborations alongside teacher-led participatory action research to build professional capacity that invites and authentically supports parent partnerships in schooling.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.027 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".