Do-It-Yourself laboratories, communities of practice, and open innovation in a digitalised environment
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.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.075 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".