Bridging distance: Practical and pedagogical implications of virtual Makerspaces
Bibliographic record
Abstract
Abstract Makerspaces are locations where people with common interests can work on projects, share ideas, tools and expertise to make or create. There is an abundance of “how to” guides and research studies on physical makerspaces, little research focuses on describing the virtual making processes and the experiences therein. This qualitative study explores the experiences of seven participants who engaged in a synchronous virtual makerspace. Meeting once a month over 16 weeks, members of the International Maker Educator Network participated in the making of a robot. This case study describes how the virtual making occurred, the personal experiences of the makers, technology used to support virtual making, and the affordances and inhibitors of virtual making. Data are analysed through the lens of a professional learning community and the People, Means and Activities makerspace framework. The paper concludes with implications for virtual making in practice and future research opportunities.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".