Evaluation of a Family and Community Engagement Strategy in Three Ontario Communities
Bibliographic record
Abstract
The Learning Partnership (TLP) initiated a Family and Community Engagement Strategy (FACES) initiative in three Ontario communities to foster active and responsive relationships among community partners and enhanced family engagement in transitions to school. A case study research design, grounded in participatory action research, was used to describe the processes and activities undertaken by the three communities. Findings indicate that social capital (Block, 2009) was increased through a unified focus on the needs of children, strong local leadership, collaboration among community partners, and effective strategies embedding FACES into the culture of the community. Le Partenariat en éducation a initié une stratégie (Family and Community Engagement Strategy – FACES) dans trois communautés en Ontario de sorte à favoriser, d’une part, des relations actives et dynamiques parmi les partenaires de la communauté et, d’autre part, l’implication de la famille dans la transition vers l’école. Suivant le plan de recherche d’une étude de cas reposant sur la participation active, nous avons décrit les démarches et les activités entreprises par les trois communautés. Les résultats indiquent que le capital social (Black, 2009) a augmenté en raison d’une orientation commune concentrée sur les besoins des enfants, un leadership local solide, la collaboration entre les partenaires communautaires et des stratégies efficaces intégrant FACES dans la culture de la communauté.
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 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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".