Microfoundations and dynamics of do-it-yourself ecosystems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it