Making the North, Together: Envisioning makerspaces as systems conveners in the social economy
Bibliographic record
Abstract
This major research project (MRP) applies systems and foresight tools to the realm of makerspaces and their social impact – it seeks to understand the role of a makerspace in convening community and facilitating self-organization from the grassroots level. It asks how the democratization of making, and of the tools and technologies involved, plays a role fostering the inherent creativity and niche innovations of a community. Hypothesizing that makerspaces, have the potential to reach across different spectrums of socio-economic class and identity, how might they act as leaders within a system, engines for convening grassroots power? Where social systems have become entrenched, in what ways might makerspaces exert pressure on existing regimes? These questions are applied in an action case following a participatory action research methodology, sponsored by a not-for-profit maker-space in Yellowknife, Canada, called MakerspaceYK (MSYK). Following the acquisition of a new space and resources, the sponsor sought a generative re-framing of its strategic purpose, \nespecially in relation to the systemic issues faced by the community it serves. The organization’s perspective on its role within the wider system was explored strategic foresight tools, and interviews were conducted with other local non-profit and social impact organizations to establish the systemic landscape. The research findings were consolidated and synthesized into a Theory for Systemic Change and Action, with the aim of understanding potential impact and latent systemic leverage. \nUltimately, the study finds that makerspaces espouse the unique quality of being able to scale to purpose, reaching across the system as an intermediary, coordinator, and resource orchestrator among regime-level, niche-level, and community level actors. Due to this quality, makerspaces are well positioned to become systems conveners – fostering dialogue, spaces for learning, and cooperation across social boundaries.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.049 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".