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Record W4405642008 · doi:10.3390/socsci13120695

Contextualised, Not Neoliberalised, Approaches to Families in Five Countries: Quality and Practice

2024· article· en· W4405642008 on OpenAlexaffabout
Marg Rogers, Fabio Dovigo, Astrid Mus Rasmussen, Khatuna Dolidze, Laura Doan

Bibliographic record

VenueSocial Sciences · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsThompson Rivers University
FundersUniversity of New England
KeywordsGeneral partnershipThematic analysisCurriculumGovernment (linguistics)NegotiationQualitative propertyDescriptive statisticsPedagogyPsychologyMedical educationPublic relationsSociologyQualitative researchPolitical scienceMedicineSocial scienceComputer science

Abstract

fetched live from OpenAlex

Partnerships with parents in early childhood education and care services are a hallmark of quality education. Educators in Western countries work within a highly regulated environment, where government documents, such as frameworks, standards, and curricula, direct most of their work, time, and energy. Despite this, data from our mixed methods online survey from Australia, Canada, Denmark, Georgia, and Italy revealed a strong resistance to the homogeneity these documents prescribe. For the quantitative data, we used cross-tabulation and descriptive statistics. For the qualitative data, we used deductive thematic analysis using a parent–educator partnership framework. Educators described parents in their service as partners in their child’s education. This included efforts to share information, consult, negotiate, and build partnerships; problem solve; and monitor, report and manage the partnership. The educators talked about the uniqueness of their approaches to parents and families within their contextualised services. They then revealed how these unique features impacted their notions of quality and practice in these services. This will be of interest to policymakers, educators, and teacher educators.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.520
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.416
GPT teacher head0.470
Teacher spread0.054 · 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 teacher head, 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

Citations2
Published2024
Admission routes2
Has abstractyes

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