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Record W2151186456 · doi:10.1017/s0260210513000144

Postnational discourse, deliberation, and participation toward global risk governance

2013· article· en· W2151186456 on OpenAlexaff
Andreas Klinke

Bibliographic record

VenueReview of International Studies · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsDeliberationNormativeAmbiguityPoliticsGlobal governancePolitical scienceCorporate governanceKey (lock)SociologyEpistemologyLawEconomicsComputer science

Abstract

fetched live from OpenAlex

Abstract An emerging task in world politics is to cope with human-induced global risks in domains such as environment, economy, security, and health. Current global governance institutions are largely incapable of tackling global risks and applying deductive policy models, which is why new modes of interaction may become essential. In this article I argue that through focused discourses, key peculiarities of global risks, namely complexity, scientific uncertainty and sociopolitical ambiguity, may be identified and understood. To this end, distinctively discursive and pragmatic learning processes can be developed. Different forms of deliberation and participation help develop processes that meet the challenges, problems, and conflicts that result from the key peculiarities of global risks. Hence, the article establishes a causal link between key peculiarities of global risks and postnational discourses. I discuss the varying forms of deliberation and participation (epistemic institutions, associational policy making, and transnational public deliberation and participation) of three discourses that produce institutional problem solving capacity in global risk governance. To this end, this article links theory and practice as well as normative conceptualisation and institutional feasibility.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.791
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.033
GPT teacher head0.408
Teacher spread0.375 · 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 designObservational
Domainnot available
GenreReview

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

Citations14
Published2013
Admission routes1
Has abstractyes

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