From Open Innovation to Crowd Sourcing: A New Configuration of Collaborative Work?
Bibliographic record
Abstract
In the era of the digital economy (ICT, Internet Objects, Cloud, Big and Open Data, etc.), we observe important transformations linked to this digital revolution [1], including development of collaborative and participative platforms, the rise of inter-company, inter-organization and inter-network collaborations, as well as the development of sharing and open innovation dynamics (crowd sourcing, crowd funding, maker space, Fab Lab, Innovation Laboratory Open, etc.). We wanted to better understand how innovation was developed in this context and to this end, we conducted a thorough study of an open-value network aimed at developing innovative products. The network studied, Sensorica, is organized around three fundamental pillars, each with a specific role: an association, the NPO, for governance, a network of companies for commercialization and an open, international community for collaborative work and the development of innovation. It is thanks to a platform on the internet that individual workers, motivated by the values of the peer to peer (P2P) or participative economy are involved in creating together innovations on distributed projects. In the context of participatory economics, this network illustrates new forms of cooperation, ways of managing collaborations based on the model of P2P, based on a partnership of shared values system.
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.
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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.014 | 0.083 |
| Scholarly communication | 0.031 | 0.060 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".