Learning to Enhance Community-Responsiveness in an Out-of-School Club Program
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".