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Record W4360982191 · doi:10.1177/16094069221132182

Learning to Enhance Community-Responsiveness in an Out-of-School Club Program

2023· article· en· W4360982191 on OpenAlexaff
Lydia Burke

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsClubGeneral partnershipContext (archaeology)Public relationsSociologyPedagogyComputer scienceMedical educationPsychologyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

This article describes a methodology and method that can be adopted by informal education leaders who are interested in establishing or developing a community-responsive focus for out-of-school club programming. Based on an adaptation of Dewey’s Laboratory School model, a university research team partnered with a community-based science club provider (the STEM Academy) to establish a model club space where ways of enhancing community-responsiveness could be explored. The overall aim was to scale up an iterative practice of research-informed adaptations into a program of over 20 clubs. The partnership was focused on a two-phase process, equipping the STEM Academy to continue an ongoing research practice in the absence of university influence. The first phase of the study involved gathering information regarding science needs, wants and preferences of a new community context. The second phase established a model club space and sought community feedback on the efficacy of the club in meeting community desires. The research approach used to establish the club and gather ongoing data is described in this article which ends by proposing a schema that can be adapted to suit other out-of-school clubs and programs.

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)0.033
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0330.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.563
GPT teacher head0.690
Teacher spread0.126 · 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; both teacher heads agree on what is shown here.

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
Published2023
Admission routes1
Has abstractyes

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