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Record W2766861252 · doi:10.1093/reseval/rvx037

Using contribution analysis to evaluate the impacts of research on policy: Getting to ‘good enough’

2017· article· en· W2766861252 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueResearch Evaluation · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsImpactUniversity of Waterloo
FundersUniversity of WaterlooCanadian Cancer Society
KeywordsManagement scienceRegional scienceOperations researchPublic economicsPolitical sciencePsychologySociologyEconomicsMathematics

Abstract

fetched live from OpenAlex

Assessing societal impacts of research is more difficult than assessing advances in knowledge. Methods to evaluate research impact on policy processes and outcomes are especially underdeveloped, and are needed to optimize the influence of research on policy for addressing complex issues such as chronic diseases. Contribution analysis (CA), a theory-based approach to evaluation, holds promise under these conditions of complexity. Yet applications of CA for this purpose are limited, and methods are needed to strengthen contribution claims and ensure CA is practical to implement. This article reports the experience of a public health research center in Canada that applied CA to evaluate the impacts of its research on policy changes. The main goal was to experiment with methods that were relevant to CA objectives, sufficiently rigorous for making credible claims, and feasible. Methods were ‘good enough’ if they achieved all three attributes. Three cases on government policy in tobacco control were examined: creation of smoke-free multiunit dwellings, creation of smoke-free outdoor spaces, and regulation of flavored tobacco products. Getting to ‘good enough’ required careful selection of nested theories of change; strategic use of social science theories, as well as quantitative and qualitative data from diverse sources; and complementary methods to assemble and analyze evidence for testing the nested theories of change. Some methods reinforced existing good practice standards for CA, and others were adaptations or extensions of them. Our experience may inform efforts to influence policy with research, evaluate research impacts on policy using CA, and apply CA more broadly.

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.

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: Empirical
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
gptMetaresearch
Domain: Evaluation · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
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)0.296
metaresearch head score (Gemma)0.145
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2960.145
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.008
Science and technology studies0.0030.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.823
GPT teacher head0.763
Teacher spread0.061 · 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