MétaCan
Menu
Back to cohort
Record W2746050179 · doi:10.1111/ropr.12258

Nondemarcated Spaces of Knowledge‐Informed Policy Making: How Useful Is the Concept of Boundary Organization in IR?

2017· article· en· W2746050179 on OpenAlexaff
Daniel Compagnon, Steven Bernstein

Bibliographic record

VenueReview of Policy Research · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCoproductionBoundary-workScience policyUSableBoundary objectSociologyKnowledge productionWork (physics)EpistemologySociology of scientific knowledgePoliticsInternational relationsValue (mathematics)Knowledge managementPolitical sciencePublic relationsSocial scienceComputer scienceNegotiationPublic administrationLaw

Abstract

fetched live from OpenAlex

Abstract Concepts of “boundary organization” and “boundary work,” borrowed from science and technology studies (STS), are now commonly used in International Relations to analyze organizations providing a science–policy interface. This article critically examines these concepts, with close attention to specific insights from the STS literature, for their added value in understanding the interactions between knowledge production processes on the one hand and policy making at the global level on the other. It lays the basis for two critiques: (1) an institutionalist critique of the use of these metaphors highlighting the mismatch between the interplay of relevant actors—scientists, policy makers, and stakeholders—via the social spaces they occupy and international organizations; (2) on weak assumptions on coproduction. The authors argue that the true challenge for science–policy interfaces is to generate politically “usable knowledge” and conditions for social learning, thus recognizing that politicization of science is more likely than the scientification of politics.

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

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)0.002
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.092
GPT teacher head0.443
Teacher spread0.351 · 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

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations16
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueReview of Policy ResearchSame topicSustainability and Climate Change GovernanceFrench-language works237,207