Gestion des relations familles immigrantes-écoles-communautés en temps de pandémie de COVID-19 : une analyse de documents
Bibliographic record
Abstract
In the pre-pandemic period, school headmasters were responsible for ensuring the academic and social integration of immigrant students by promoting innovation, relations within the educational team and school-family-community relations (MELS, 2008 ; MÉES, 2019). However, the pandemic and the massive use of Information and Communication Technologies (ICT) has led to a deterioration in these relations, resulting in performance-related health problems and unforeseen changes (OECD, 2020). The initial question in this article is: how have school headmasters managed the school-family-community relations embedded in the activity of accompanying immigrant students fostered by the use of ICT in a context of unplanned change? A systematic literature review identified 43 empirical studies. Their grounded theory analyses led to the modelling of problems and their theorisation. This systematic review confirms the importance of school-family-community relations and their management for the academic and educational success of immigrant students during a pandemic. The underlying problems of relationship management include problems of educational quality; divergence of viewpoints in terms of performance; development of psychosocial risks at work linked to these divergences and to social inequalities (digital inequalities, gender inequalities in children's education within the family, housing inequality).
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".