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Record W2587072002 · doi:10.5751/es-03623-150430

The Use of Impact Assessment Tools to Support Sustainable Policy Objectives in Europe

2010· article· en· W2587072002 on OpenAlexvenueno aff
Peter De Smedt

Bibliographic record

VenueEcology and Society · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental resource managementImpact assessmentEnvironmental planningBusinessGeographyPolitical scienceEnvironmental sciencePublic administration

Abstract

fetched live from OpenAlex

Sustainable development has become an overall policy objective in Europe. The sustainability transition is seen as the process of coming to terms with sustainability in all its ecological, social, economic, and institutional dimensions. This challenging process is as much about new ways of knowing as it is about resource management and product innovation. I analyzed how scientific tools such as environmental, socioeconomic, and integrated models have been developed and used to provide a solid foundation for sustainable policy objectives. I used a scoping study to compare current impact assessment exercises and research policy cases within the European Commission (EC). Although the EC is recognized as having supported environmental policy integration for more than three decades, updated insight is relevant because the current impact assessment system within the EC is highly dynamic, involving frequent new policy commitments and institutional initiatives to strengthen its practice and quality within the last three years. In addition, a broad range of research projects have been initiated to support this process by developing new concepts and tools and linking them with actual policy initiatives. Finally, the focus on design and use of scientific tools will contribute to a better understanding of what hinders the tools from being relevant, accurate, and legitimate. For example, while being simplifications of reality, many scientific models remain so complex that they are seen as black boxes instead of transparent analytical tools. Consequently, research outcomes do not fully reach policy makers.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.015
GPT teacher head0.319
Teacher spread0.305 · 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
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

Citations42
Published2010
Admission routes1
Has abstractyes

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