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Record W6939767751 · doi:10.6084/m9.figshare.c.6694723

Evaluating the quality of research co-production: Research Quality Plus for Co-Production (RQ + 4 Co-Pro)

2024· other· en· W6939767751 on OpenAlexaff

Bibliographic record

VenueFigshare · 2024
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsUniversity of OttawaMcGill UniversityWestern UniversityMichael Smith Health Research BCUniversity of TorontoCanadian Cancer SocietyDalhousie UniversityUniversity of ManitobaOttawa Hospital
Fundersnot available
KeywordsRelevance (law)Quality (philosophy)Thematic analysisTest (biology)Process (computing)Qualitative researchResearch designField (mathematics)

Abstract

fetched live from OpenAlex

Abstract Background Co-production is an umbrella term used to describe the process of generating knowledge through partnerships between researchers and those who will use or benefit from research. Multiple advantages of research co-production have been hypothesized, and in some cases documented, in both the academic and practice record. However, there are significant gaps in understanding how to evaluate the quality of co-production. This gap in rigorous evaluation undermines the potential of both co-production and co-producers. Methods This research tests the relevance and utility of a novel evaluation framework: Research Quality Plus for Co-Production (RQ + 4 Co-Pro). Following a co-production approach ourselves, our team collaborated to develop study objectives, questions, analysis, and results sharing strategies. We used a dyadic field-test design to execute RQ + 4 Co-Pro evaluations amongst 18 independently recruited subject matter experts. We used standardized reporting templates and qualitative interviews to collect data from field-test participants, and thematic assessment and deliberative dialogue for analysis. Main limitations include that field-test participation included only health research projects and health researchers and this will limit perspective included in the study, and, that our own co-production team does not include all potential perspectives that may add value to this work. Results The field test surfaced strong support for the relevance and utility of RQ + 4 Co-Pro as an evaluation approach and framework. Research participants shared opportunities for fine-tuning language and criteria within the prototype version, but also, for alternative uses and users of RQ + 4 Co-Pro. All research participants suggested RQ + 4 Co-Pro offered an opportunity for improving how co-production is evaluated and advanced. This facilitated our revision and publication herein of a field-tested RQ + 4 Co-Pro Framework and Assessment Instrument. Conclusion Evaluation is necessary for understanding and improving co-production, and, for ensuring co-production delivers on its promise of better health.. RQ + 4 Co-Pro provides a practical evaluation approach and framework that we invite co-producers and stewards of co-production—including the funders, publishers, and universities who increasingly encourage socially relevant research—to study, adapt, and apply.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Evaluation · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptMetaresearchScience and technology studies
Domain: Evaluation · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
models splitAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.030
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.434
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.4500.016

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.

Opus teacher head0.717
GPT teacher head0.588
Teacher spread0.128 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

MetaresearchScience and technology studies

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Other design
DomainEvaluation
GenreOther · Methods

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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