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Record W2149419459 · doi:10.3390/su7055735

Conceptualizing the Effectiveness of Sustainability Assessment in Development Cooperation

2015· article· en· W2149419459 on OpenAlexaff
Jean Hugé, Nibedita Mukherjee, Camille Fertel, Jean-Philippe Waaub, Thomas Block, Tom Waas, Nico Koedam, Farid Dahdouh‐Guebas

Bibliographic record

VenueSustainability · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsUniversité du Québec à MontréalGroup for Research in Decision AnalysisHEC Montréal
FundersVlaamse Interuniversitaire RaadFonds De La Recherche Scientifique - FNRSNational Research Foundation
KeywordsSustainabilitySustainability organizationsOperationalizationConceptualizationAcknowledgementCredibilitySustainability scienceSustainable developmentSocial sustainabilityLegitimacyProcess managementPolitical scienceBusinessComputer sciencePolitics

Abstract

fetched live from OpenAlex

Sustainability assessment has emerged as a key decision-support process in development cooperation in response to the growing acknowledgement of the impacts of global change. This paper aims at conceptualizing the effectiveness of sustainability assessment as applied in development cooperation, by focusing on the sustainability assessment practice by actors of the official Belgian Development Cooperation. The conceptualization of the effectiveness of sustainability assessment is synthesized in a set of issues and concerns, based on semi-structured interviews. The paper highlights the specificity of sustainability assessment in the development cooperation sector (e.g., through the cultural and discursive compatibility dimensions of assessment in a North-South context). Effectiveness is inherently linked to the expected functions of sustainability assessment in the decision-making process, which include fostering organizational change, shaping contextually adapted framings of sustainability and operationalizing the sustainability transition. These findings highlight the relevance of a discourse-sensitive approach to sustainability assessment if one is to strengthen its credibility and legitimacy.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.016
GPT teacher head0.322
Teacher spread0.306 · 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

Citations13
Published2015
Admission routes1
Has abstractyes

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