Museum Facilitator Practice as Infrastructure Design Work for Public Computing
Bibliographic record
Abstract
In this paper, we emphasize the importance of looking beyond technology itself and including interactional and experiential elements in our research gaze in informal computing education in science museums. We argue that, in these contexts, facilitation can be understood as design work that is both complex and challenging. We identify how focusing on infrastructuring—the process by which an exhibit’s support systems emerge, shift, and are sustained in practice—can help develop a richer understanding of the complexity of this work. In this study, we examine facilitators’ experiences of facilitating and supporting a computational exhibit in a science museum. We identify how facilitators’ expertise, roles, and responsibilities shape their facilitation work. Through analysis of video-recorded interactions at the exhibit and interviews with facilitators, we showcase how facilitators’ in-the-moment design moves addressed breakdowns of the exhibit’s infrastructure. These design moves emerged from the complex interaction of each facilitator’s epistemological views of computing and museum education, values, past experiences, and disciplinary background, as well as the museum culture and other institutional constraints. This analysis represents an important challenge to technocentric stances in informal computing education with implications for informal educators and managers, as well as designers and design researchers more broadly.
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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 | 0.032 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.015 | 0.025 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".