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Record W2936993317 · doi:10.53956/jfde.2018.131

Using Participatory Action Research to Create Systematic Parent Engagement

2018· article· en· W2936993317 on OpenAlexafffundabout
Debbie Pushor

Bibliographic record

VenueJournal of Family Diversity in Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsUniversity of Saskatchewan
FundersSocial Sciences and Humanities Research Council of CanadaCollege of Education, University of Saskatchewan
KeywordsParticipatory action researchHonorCitizen journalismValue (mathematics)Action (physics)Action researchCommunity engagementSociologyPublic relationsOrder (exchange)Engineering ethicsPolitical sciencePedagogyBusinessEngineering

Abstract

fetched live from OpenAlex

Despite ample research supporting the value of strong family, school, and community partnerships, few districts or communities have been able to support and sustain systematic parent engagement. This article focuses on the efforts undertaken by a research team in Saskatoon, Canada to develop a model that empowers parents to systematically engage with educators and schools in order to enhance educational and social outcomes for children and parents. Central to this effort has been the use of participatory action research (PAR) as a means to honor and engage the experiences of diverse community members. This article highlights specific PAR methods that supported the development of a more systematic approach to parent engagement. The experiences described demonstrate how the improvisatory and responsive nature of the PAR approach help to both build trust and create more sustainable changes in communities.

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)0.182
metaresearch head score (Gemma)0.107
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: none
Teacher disagreement score0.182
Threshold uncertainty score0.963

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1820.107
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0080.014
Scholarly communication0.0100.007
Open science0.0040.019
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.755
GPT teacher head0.569
Teacher spread0.187 · 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

Citations13
Published2018
Admission routes3
Has abstractyes

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