Microfoundations and dynamics of do-it-yourself ecosystems
Bibliographic record
Abstract
Small-scale do-it-yourself (DIY) practices have driven emerging user communities and global movements. As research on ecosystems has proliferated, limited insights have been generated on the interdependent and dynamic nature of DIY ecosystems. Drawing on observations of a locally established space for DIY activities (“makerspace”) with international networks, a flexible pattern matching approach was adopted in explaining how disparate projects played a primary role in the formation of a self-sustaining DIY ecosystem with interdependent start-up actors, or “makers”. Two patterns were drawn from the literature on DIY ecosystems to discover matches and mismatches in longitudinal data that were drawn from a coworking-space in Shenzhen, China. The findings suggest two emergent dimensions: internal alignment, and connection with, and resilience to, the ecosystem's external environment. We explain how these dimensions advance understanding of DIY ecosystems by illuminating their interdependent and self-sustaining nature. Policy recommendations are also offered in supporting the development particularly of user communities in makerspaces.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".