Comprendre , dévoiler, initier : contribuer par le design à l’initiation d’un écosystème
Bibliographic record
Abstract
In the Bas-Saint-Laurent region of Quebec, managing plastic waste poses a number of challenges. Recent work by a biotechnology research group has led to the identification of a local bacterium capable of degrading certain plastics. As part of a drive for social innovation, the researchers have joined forces with an open innovation laboratory to set up a regional innovation ecosystem and ensure the best possible implementation of this solution. Few studies have documented the tools used to initiate ecosystems, and the research project presented here aims to test, prototype and question mediation tools and methods to encourage stakeholders to become involved in this future ecosystem. This paper questions the narrative and scenaristic scope of design and its ability to bring out the aims and values of a systemic innovation. It demonstrates, through practice, that design enables an approach to be taken at different levels of an ecosystem in the making, and helps to guide decisionmaking by revealing the value horizon and the potential for evolution of the solution it supports.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".