Boundary play and pivots in public computation: new directions in STEM education
Bibliographic record
Abstract
In this paper, we introduce ‘‘public computation’’ as a genre of learning environments that can be used to radically broadenpublic participation in authentic, computation-enabled STEM disciplinary practices. Our paradigmatic approach utilizesopen-source software designed for professional scientists, engineers and digital artists, and situates them in an undilutedform, alongside live and archived expert support, in a public space. We present case studies in DigiPlay, a prototypicalpublic computation space we designed at the University of Calgary, where users can interact directly with scientificsimulations as well as the underlying open source code using an array of massive multi-touch screens. We argue that in sucha space, public interactions with the code can be thought of as ‘‘boundary work and play’’, through which publicparticipation becomes legitimate scientific act, as the public engages in the invention of novel scientific creation throughtruly open-ended explorations with pivotal elements of the code.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.051 |
| Scholarly communication | 0.021 | 0.031 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".