Do-It-Yourself laboratories, communities of practice, and open innovation in a digitalised environment
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
A growing literature has explored the role of innovation as a driver of healthy economies. We discuss the role of Do-It-Yourself laboratories (‘DIY labs’) in driving open innovation. Digitalisation, in terms of faster, broader, and more easily accessible internet connectivity, has enabled private and public DIY labs to flourish, and to form online, practice-led Communities of Practice (‘COPs’). The phenomena of in-person DIY labs and online COPs seem to be part of a societal shift from centralised research and development departments in large organisations to democratic, user-led cyberspaces where ideas and innovations are generated by well-educated and well-connected participants. We argue that DIY labs address un-met market demands by individualising mass market products, processes, and services. We extend the COP lens by theorising on the effects of digitalisation on the advantageous interaction of COP members with DIY labs. We suggest how this interaction has significant social and economic implications, particularly in the ways that innovation activity in public spaces and organisations may be used and rewarded.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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