“It’s Hard for Friends to Say No”: Principals’, Teachers’ and Community Members’ Use of Social Networks for School-Community Collaboration
Bibliographic record
Abstract
This qualitative comparative case study examines the role of social networks and social capital in developing school-community relationships and collaborative activities. Eighty-one participants (directors of education, superintendents, administrators, teachers, parents, community members) from two districts in the same southern Ontario region participated in individual open-ended, 45-minute interviews. Observations were conducted, and documents were collected. Principals were essential to the establishment of a school culture that welcomed family-school-community collaboration and were frequently the interpreters of community involvement policy while teachers were the implementers of school-community activities, often initiating partnerships. Differences in quantity and quality of community involvement in the two districts yielded insights into collaborative processes. Material, human and financial resources were more easily accessed through existing social relationships, in which trust was already cultivated. Without the support of board-level administrators, principals and teachers, community involvement was limited. Findings extend research by identifying the importance of social networks and the key role of a liaison with the ability to craft collaborative relationships that benefit all parties.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".