A framework for school-family collaboration integrating some relevant factors and processes
Bibliographic record
Abstract
Collaboration between the school and the family is increasingly privileged as one of the means to promote educational success and\nperseverance. This is based among other things on a sharing of responsibilities between parents and teachers. Although knowledge\nhas evolved in relation to collaborative school-family relationships, it has far from developed in all Québec schools. The division of\nresponsibilities appears more rhetorical than practical. More work must be done. The objectives of this paper are to (1) conduct an\noverview of parental involvement and school-family collaboration literature under the angles of concept definitions and influential\nfactors; and drawing on Hoover-Dempsey et al.’s models (1997, 2010), and 2) to propose an integrative model of factors and processes\nlinked to parental involvement and school-family collaboration. Given that the challenges facing teachers appear to have increased\nexponentially and that parents’ conditions for exercising their role have become more complex, it appears to be timely to have parents\nand teachers sitting together and share their vision in order to develop a common understanding and a collective vision of the current\nsituation regarding school-family collaboration. The proposed integrative framework is intended to provide a tool to the main actors\neager to engage in a reflective activity
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".