A critical analysis of the Assessment and Action Record (AAR) documentation: Examining the educational experiences of Black youth-in-care in Ontario
Bibliographic record
Abstract
This article focuses on the Ontario Assessment and Action Record (AAR), used in child welfare to understand how this documentation supports (and fails to support) Black youth-in-care and their academic needs. We applied a critical review and analysis of three distinct but interconnected sources of data: 1) the AAR-C2-2016; 2) literature on the education of Black youth-in-care in Ontario; 3) policy and agency documents concerning how this group is faring. In our analysis of the AAR and its education dimension, findings suggest the AAR has been a race-neutral tool, which has implications in terms of how we conceptualize structural barriers faced by Black children and youth-in-care. We identified gaps and potential practice dilemmas for child welfare workers when using AAR documentation procedures. Using Critical Race Theory and the United Nations human rights framework, we argue that the AAR can be a tool to identify, monitor, and challenge oppression for Black children and youth-in-care who experience a continual negotiation of racialization alongside being a foster child. The AAR recordings can be harmful if they are simply a collection of information on the key areas of a child’s life. Prioritizing the academic needs of Black children in care is critical to social work and aligns with the commitments of One Vision, One Voice, Ontario’s Anti-Racism Strategic Plan as well as the United Nations Convention on the Rights of the Child, particularly in relation to the right to education.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".