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Record W2004361063 · doi:10.1080/15239080701622881

Steering for Sustainable Development: a Typology of Problems and Strategies with respect to Ambivalence, Uncertainty and Distributed Power

2007· article· en· W2004361063 on OpenAlexaff
Jan-Peter Voß, Jens Newig, Britta Kastens, Jochen Monstadt, Benjamin Nölting

Bibliographic record

VenueJournal of Environmental Policy & Planning · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsYork University
Fundersnot available
KeywordsTypologyAmbivalenceSustainable developmentSustainabilityIdeal (ethics)Corporate governancePower (physics)Development (topology)Computer scienceSociologyManagement scienceRisk analysis (engineering)BusinessEconomicsPolitical scienceSocial psychologyPsychologyManagementMathematicsLawEcology

Abstract

fetched live from OpenAlex

Special features of sustainable development as a governance problem are contrasted with a conventional rationalist ideal of steering based on the unambiguous determination of goals, availability of knowledge to predict consequences and concentration of power to implement strategies. This leads into the elaboration of three problem dimensions of steering for sustainable development: ambivalence of sustainability as a goal, uncertainty of knowledge due to complex interactions between society, technology and nature, and distributed power to shape structural change in society. The problem dimensions are taken as a basis for a typology of steering situations and a review of existing theoretical concepts of steering in society. The paper argues for a differentiated discussion of steering capacities in respect to concrete situations. Along these lines, it presents an approach to match strategies with problems.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.006
Science and technology studies0.0040.041
Scholarly communication0.0160.017
Open science0.0020.007
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.013
GPT teacher head0.258
Teacher spread0.245 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations207
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Environmental Policy & PlanningSame topicSustainability and Climate Change GovernanceFrench-language works237,207