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Record W1970077003 · doi:10.14507/epaa.v8n52.2000

Support for parent participation in primary school

2000· article· en· W1970077003 on OpenAlexaffabout
Judith K. Bernhard, Marlinda Freire, Veronica Pacini-Ketchabaw

Bibliographic record

VenueEducation Policy Analysis Archives · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of TorontoToronto Metropolitan University
Fundersnot available
KeywordsMainstreamEthnic groupLatin AmericansInstitutionExploratory researchRace (biology)PsychologySociologyGender studiesPedagogySocial psychologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

This article describes how a group of Latin American parents became more effective in their dealings with their children’s schools, a mainstream Canadian institution. Ethnicity, along with race, gender, and social class, is a critical determinant in of the interactions between schools and any group of newcomers to a society, particularly when those newcomers are an ethnic minority. Over an eight-month period, twelve Latin American parents met monthly to discuss aspects of their children’s experience with the Canadian educational system. These parents learned to collaborate with teachers and expressed their needs, but also affirmed their ethno-cultural differences. The positive feedback on their activities led to unforeseen gains, not just in relation to education and the schools. This exploratory study focuses on how the experience helped the parents to better comprehend what is expected of them in the support of their children’s schooling while retaining their own cultural assets. This study may serve as a possible model of adaptation for newcomer groups in their efforts to integrate in the school system.

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)0.003
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.000

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 head0.047
GPT teacher head0.430
Teacher spread0.383 · 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

Citations1
Published2000
Admission routes2
Has abstractyes

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