Manufacturing Bad Workers: How Oppressive Practices in Child Protection are Maintained
Bibliographic record
Abstract
This research seeks to better understand the institutional mechanisms that work to maintain oppressive practices in child protection social work settings. This research examines the ways in which the child protection system continues to function in discriminatory and punitive ways that are not reliably effective at keeping children safe from abuse or neglect. These oppressive functions remain despite what can be presumed to be the best of intentions of legislators, policy makers, administrators, and child protection social workers (CPW), and despite the countless legislative, policy, and practice changes that have been embarked upon to reform child protection in Ontario in the past. Using methods informed by Institutional Ethnography (IE), this research examines how the various attempts at reform to the child protection system in Ontario have changed the ways that frontline child protection workers carry out their work and interact with their service recipients. In addition to an IE approach, this research incorporates autoethnography to include direct experiences and observations as a means to unpack some of the ways that frontline CPWs experience, respond to, and are complicit with oppressive practices. Also included is an examination of a recent reform in Ontario, the implementation of the Child Protection Information Network (CPIN) between 2014 and 2019. CPIN provides an opportunity to examine how child protection work is organized, and some of the ways that CPWs themselves are recruited into the ‘work’ of maintaining the status quo. Participants shared their experiences around the ways that CPIN integrated neoliberal rationalities, solidifying commitments to cost-saving, efficiency, and heavy regulation. Rather than disrupting structures of oppression endemic to the systems of child protection, reform efforts like CPIN serve to maintain and even further entrench disparities. To sustain itself, child welfare draws CPWs into understandings of ostensible benevolence as ‘child saviours’, fostering relational distance that allows for and enables dehumanization, and employs rigorous and fear-motivated accountability circuits that keep workers in line, while punishing those who fall outside them. In other words, in its current form, the child welfare system, to sustain itself, must be invested in manufacturing “bad” 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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.045 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".