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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".