Bibliographic record
Abstract
Co-creation – the joint production of innovation between combinations of industry, research, government and civil society – was widely used to respond to the challenges raised by the COVID-19 pandemic. This paper describes 30 COVID-19 co-creation initiatives from 21 countries and three international cases. The template focuses on initiatives’ core characteristics, including information on key co-creation partners and their contributions, key outcomes as well as the initiatives’ size. The comparative evidence gathered through interviews with case study initiative leaders also describes what co-creation instruments were used, how networks leading to the collaboration were built, what type of cross-disciplinary co-operation took place, and what role governments played in the process and the procedures adopted to deal with the COVID-19 “exceptionality”, including the urgency of producing implementable solutions. The information gathered provides a basis for analyses on co-creation initiatives during COVID-19 and for drawing potential policy implications.
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 | 0.033 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.030 |
| Research integrity | 0.003 | 0.005 |
| 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".