Data justice for youth in and leaving care: mapping the child welfare data landscape in Ontario
Bibliographic record
Abstract
The digitization of social services provides the public sector with new tools to monitor and meet managerial and legislative objectives. But these practices re-shape service provision and the experiences of those receiving social welfare interventions. This article reports on results from phase one of an institutional ethnography of public sector policy, knowledge, and technology. We begin by describing our iterative mapping methodology. We then share preliminary results of our efforts to investigate the socio-technical processes that shape people’s experiences on the frontlines of child welfare agencies in Ontario Canada –those who are the targets and recipients of these services and those involved in service delivery and governance. Results include a map of child welfare data holdings, as well as a synthesis of key informants’ concerns about how and whether the provincial child welfare information management and policy landscape enables their legislative duty to promote the best interest, protection, and wellbeing of youth. Results suggest data holdings are compromised by methodological and infrastructural issues that undermine the utility of the Child Protection Information Network for clinical practice as well as for monitoring systemic trends.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".