Best bang for your buck: Considerations for cost-efficiency in knowledge co-production
Bibliographic record
Abstract
Knowledge co-production is a key strategy of collaborative engagement between research and management to achieve better outcomes. Whereas the principles of knowledge co-production in support of evidence-informed policy-making are increasingly understood, our understanding of its cost-efficiency - putting benefits in relation to its cost - is in its infancy. Here, we approach this gap by exploring the key considerations for ensuring that the benefits of co-production processes outweigh the significant direct and indirect costs they can incur. We conceptualise a relationship between the costs and benefits of co-production, consider preconditions that affect those costs and benefits, and outline options for improving the cost-benefit relationship. Specifically, we explore how to maximise co-production efficiency for key principles underpinning effective knowledge co-production (context-based, pluralistic, goal-oriented, interactive) and illustrate this with a hypothetical case study of co-production for the use and management of an emerging small-scale fishery. To this end, we conclude by providing a series of guiding questions that practitioners of co-production can use to help ensure that the benefits outweigh the costs. Our results provide researchers and practitioners with improved understanding of the costs and benefits of co-production and encourage the consideration of cost-efficiency in the planning of participatory research. Further, by considering the costs and benefits of co-production processes we provide critical insights into how to ensure effective and efficient science-policy engagement where expectations might exceed limited resources. This includes enabling more transparent and accountable funding and engagement decisions while engaging multiple context-specific streams of policy-relevant knowledge for evidence-informed policy.
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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 | 0.152 | 0.250 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.062 |
| Scholarly communication | 0.040 | 0.062 |
| Open science | 0.007 | 0.025 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".