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Taking Boundary Work Seriously: Towards a Systemic Approach to the Analysis of Interactions Between Knowledge Production and Decision-Making on Sustainable Development

2012· book-chapter· en· W29722832 on OpenAlexaff
Stefan Jungcurt

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsInternational Institute for Sustainable Development
Fundersnot available
KeywordsNegotiationBoundary-workBoundary (topology)Representation (politics)Work (physics)Management scienceSpace (punctuation)Diversity (politics)Production (economics)Boundary objectKnowledge managementSustainable developmentComputer sciencePolitical scienceProcess managementSociologyMathematicsEngineeringSocial scienceEconomicsPolitics

Abstract

fetched live from OpenAlex

The concept of boundary work has been put forward as an analytical approach towards the study of interactions between science and policy. While the concept has been useful as a case-study approach, there are several weaknesses and constraints when using the concept in a more systemic analysis of the interactions between knowledge production and sustainable development decision-making at the international level, such as its inability to capture the diversity of institutions involved in such boundary work. Another inability involves a lack of conceptualisation of the impacts of the specific conditions of intergovernmental decision-making, such as rules for representation and the mode of negotiation. This chapter suggests complementing the concept of boundary work with a configuration approach based on a two-dimensional conceptualisation of the boundary space in international decision-making that allows the positioning of institutions with regard to their degree of politicisation and their position in terms of national and regional representation. Such an approach could be a useful guide in the further conceptualisation and application of the boundary concept.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.032
GPT teacher head0.276
Teacher spread0.243 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations27
Published2012
Admission routes1
Has abstractyes

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