Who Makes a Makerspace?Makerspace Governance in Toronto, Ontario, and London, Ontario
Bibliographic record
Abstract
As Makerspaces have emerged under different names, including Hackerspaces, Hacklabs and FabLabs, research has studied the meaning and impacts of their practices. This thesis investigates how Makerspace’s activities are the product of complex governance networks using observation and semi-structured interviews of eight Makerspaces in Toronto, Ontario, and London, Ontario. The study found that the investigated Makerspaces’ governance networks were comprised of a wide variety of actors, ranging from non-human actors (noise, space, fumes, and tools) to formal governing institutions. The study also found that the municipal governments involved in the process were involved in a steering and guiding capacity to influence Makerspaces to move toward economic development priorities, but did not exercise their regulatory power in any observed cases. These findings highlight that research on Makerspaces should consider moving beyond practices and toward how spaces are governed in unique contexts, as the practices of Makerspaces are dependent on their governance network.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 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".