Expanding and Focusing Infrastructuring Analysis for Informal STEM Education
Bibliographic record
Abstract
In our efforts to build transformative informal STEM learning environments, we must consider how innovative educational practices and tools are adaptable, sustainable, and equitable. The lens of infrastructuring allows us to attend to the ways that people, practices, and objects already present in these environments can be leveraged and redesigned to support equitable learning outcomes. Through qualitative analysis of 16 facilitator interviews across three informal STEM organizations, we determined six types of infrastructure that support engagement with computational tinkering in informal learning environments: institutional routines and resources, social and facilitation practices, institutional and facilitator values, facilitator expertise, tools and materials, and physical space. We also point out some critical gaps or challenges within these categories that can serve as points for reflection and redesign. This work has implications for researchers, designers, and facilitators/managers who work in informal STEM settings and aim to engage learners with STEM in new ways.
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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 | 0.020 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".