Evidence map›Paper›PMID 39122844›Full record

ArticleScientific reports2024

A model of faulty and faultless disagreement for post-hoc assessments of knowledge utilization in evidence-based policymaking.

Remco Heesen, Hannah Rubin, Mike D Schneider, Katie Woolaston, Alejandro Bortolus, Emelda E Chukwu, Ricardo Kaufer, Veli Mitova, Anne Schwenkenbecher, Evangelina Schwindt and 3 more

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Remco Heesen *Department of Philosophy, Logic and Scientific Method, London School of Economics and Political Science, London, UK. r.heesen@lse.ac.uk.
Hannah Rubin *Department of Philosophy, University of Missouri, Columbia, USA.
Mike D Schneider *Department of Philosophy, University of Missouri, Columbia, USA.
Katie Woolaston *School of Law, Queensland University of Technology, Brisbane, Australia.
Alejandro BortolusInstituto Patagónico Para El Estudio de los Ecosistemas Continentales (IPEEC-CONICET), Puerto Madryn, Argentina.
Emelda E ChukwuCenter for Infectious Diseases Research, Nigerian Institute of Medical Research, Lagos, Nigeria.
Ricardo KauferCenter for Interdisciplinary Research (ZiF), Bielefeld University, Bielefeld, Germany.
Veli MitovaAfrican Centre for Epistemology and Philosophy of Science, University of Johannesburg, Johannesburg, South Africa.
Anne SchwenkenbecherSchool of Humanities, Arts and Social Sciences, Murdoch University, Perth, Australia.
Evangelina SchwindtInstituto de Biología de Organismos Marinos (IBIOMAR-CONICET), Puerto Madryn, Argentina.
Helena SlanickovaFaculty of Philosophy, University of Groningen, Groningen, the Netherlands.
Temitope O SogbanmuDepartment of Zoology, University of Lagos, Lagos, Nigeria.
Chad L HewittCentre for Biosecurity Research Analysis and Synthesis, Lincoln University, Lincoln, New Zealand.

Funding

Consejo Nacional de Investigaciones Científicas y Técnicas 11220210100507CODirectorate for Social, Behavioral and Economic Sciences 2045007Nederlandse Organisatie voor Wetenschappelijk Onderzoek 024.003.025Volkswagen Foundation Norbert Elias FellowshipZentrum für interdisziplinäre Forschung, Universität Bielefeld RG2023/1
6 · The paper itself

Abstract

When evidence-based policymaking is so often mired in disagreement and controversy, how can we know if the process is meeting its stated goals? We develop a novel mathematical model to study disagreements about adequate knowledge utilization, like those regarding wild horse culling, shark drumlines and facemask policies during pandemics. We find that, when stakeholders disagree, it is frequently impossible to tell whether any party is at fault. We demonstrate the need for a distinctive kind of transparency in evidence-based policymaking, which we call transparency of reasoning. Such transparency is critical to the success of the evidence-based policy movement, as without it, we will be unable to tell whether in any instance a policy was in fact based on evidence.

Indexed as

Policy MakingCOVID-19HumansKnowledgeModels, TheoreticalPandemicsDisagreementEpistemologyEvidence-based policyTransparency

Identifiers

PMID39122844
PMCPMC11316112

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.