An overview of quantitative instruments and measures for impact in coproduction
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the quantitative measurement tools used in fields of study related to coproduction, as an approach to mobilizing knowledge, in order to inform the measurement of impact. Design/methodology/approach An overview methodology was used to synthesize the findings from prior instrument reviews, focusing on the contexts in which measurement tools have been used, the main constructs and content themes of the tools, and the extent to which the tools display promising psychometric and pragmatic qualities. Findings Eight identified reviews described 441 instruments and measures designed to capture various aspects of knowledge being mobilized among diverse research stakeholders, with 291 (66%) exhibiting relevance for impact measurement. Research limitations/implications Future studies that measure aspects of coproduction need to engage more openly and critically with psychometric and pragmatic considerations when designing, implementing and reporting on measurement tools. Practical implications Twenty-seven tools with strong measurement properties for evidencing impact in coproduction were identified, offering a starting point for scholars and practitioners engaging in partnered approaches to research, such as in professional learning networks. Originality/value Current quantitative approaches to measuring the impacts of coproduction are failing to do so in ways that are meaningful, consistent, rigorous, reproducible and equitable. This paper provides a first step to addressing this issue by exploring promising measurement tools from fields of study with theoretical similarities to coproduction.
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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 | 0.183 | 0.337 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".