MétaCan
Menu
Back to cohort
Record W1934894165 · doi:10.26522/tl.v9i1.429

Creating Links between the School and the Community Beyond its Walls: What Teachers and Principals Do to Develop and Lead School-Community Partnerships

2015· article· en· W1934894165 on OpenAlexaffvenue
Catherine Hands

Bibliographic record

VenueTeaching and Learning · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsBrock University
Fundersnot available
KeywordsGeneral partnershipContext (archaeology)CitizenshipPedagogyPublic relationsPrincipal (computer security)SociologyQualitative researchProfessional developmentFocus groupProcess (computing)Political scienceGeographySocial science

Abstract

fetched live from OpenAlex

This paper is based on a qualitative case study examining the impact of social context on school-community partnerships. Sixty-four students and school personnel at one K-12 magnet school in southern California participated in 21 open-ended, 45-minute interviews. Observations were conducted, and documents were collected. Structural, cultural and agentive issues impacted partnership establishment. Teachers and the principal valued a school culture was conducive to community involvement. They collectively developed the school’s mission and vision with a focus on global citizenship, and initiated partnerships consistent with the vision. The stages of the partnership development process are discussed, and it is argued that they are broadly applicable to the establishment of collaborative activities. Funding and networks contributed to the professional development, resources and technology needed to support partnering. Findings extend research by identifying the educators’ leadership roles in partnering, and the structures and cultures that facilitate it.

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 imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.027
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0190.015
Scholarly communication0.0130.013
Open science0.0020.010
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.183
GPT teacher head0.389
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueTeaching and LearningSame topicParental Involvement in EducationFrench-language works237,207