Research Coproduction: An Underused Pathway to Impact
Bibliographic record
Abstract
Knowledge translation and implementation science have made many advances in the last two decades. However, research is still not making expedient differences to practice, policy, and service delivery. It is time to evolve our approach to knowledge production and implementation. In this editorial we advance research coproduction as a neglected pathway to impact. Our starting point is that research impact is a function of how research is done and who is involved, arguing that researchers and non-researchers have an equal voice and role to play. We outline principles of coproduction including sharing power, valuing different sources of knowledge and viewpoints, equality, open communication, inclusivity, and mutuality. We consider implications at micro, meso, and macro system levels. In calling for this shift in the way knowledge is produced and applied, we anticipate it leading to inclusive research that more rapidly translates to better, more equitable health and care for all.
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 | 0.072 | 0.215 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.020 | 0.040 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".