Bibliographic record
Abstract
Makerspaces have grown as sites of innovation since the turn of the 21st-century, but the processes and methods by which they have directly contributed to innovation have been underexplored. Makerspaces exist as communal hi-tech workshops that draw on networks of knowledge in order to create their community, and they live and die by this community as well. Building on literature on both innovation and communication, this dissertation will examine the communities at three specific makerspaces in the Calgary area, and ethnographic fieldwork and participant observation will inform the rich text that serves as the data for the case study approach. By examining the development process at makerspaces, this research asks 1) how are the ideas and inspirations for the development of a new technology transferred between developers and their collaborators, 2) what sources of inspiration and new knowledge do the developers use for both the subjective and functional components of their design, and finally 3) what is the role of the makerspace as a third place where developers can collaborate and share ideas during the development process? This research contributes in three areas: 1) it informs current theories on innovation on the processes that involve subjective elements in the process of innovation; 2) it advances the literature on makerspaces and their communication processes, especially their study in Canada, and 3) it initiates and advocates for the development of a critical maker studies, as a counterpart to much of the literature in the area published to date.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.193 | 0.041 |
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".