LE DÉPLACEMENT EN CONTEXTE DE PROTECTION DE L’ENFANCE: : QUELS SAVOIRS PRATIQUES À L’ŒUVRE CHEZ LES FAMILLES D’ACCUEIL QUÉBÉCOISES?
Bibliographic record
Abstract
Authors denounce the significant scholarly vacuum surrounding the realities of foster families (FFs) (Boyer & Noel, 2008), and more specifically their own practices (Cébula, 2011, Turbiaux, 2010). This is in spite of the fact that their work relies on a mediation between a number of child protection stakeholders, about which little remains known. Identifying the knowledge at the core of this mediation is highly relevant, both to better understand and recognize the work of FFs, which has struggled to acquire social legitimacy, and to positively influence the displacement process in the youth protection context. This article presents the results of a master’s thesis situated in the current of analysis of professional practices and that set out to pinpoint the practical knowledge of Quebec FFs regarding the displacement of children. To support the qualitative (descriptive and exploratory) methodology, semi-structured interviews were conducted with eight FFs. The results suggest that these families mediate three forms of knowledge during displacement, namely theoretical, technical and experiential knowledge. Modelling this knowledge helps identify four types of intervention specific to FFs: technical, reception-related, ethical and institutional interventions.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".