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Record W2776075475 · doi:10.24193/jssp.2017.2.01

The Unintended Negative Consequences of Government Actions and Initiatives in Selected Environmental, Social and Economic Domains: Opportunities for Co-construction Approaches

2017· article· en· W2776075475 on OpenAlexaff
Christopher Bryant, Chérine Akkari, Antonia Bousbaine, Kénel Délusca, Oumarou Daouda, Mamadou Adama Sarr, Azzeddine Madani

Bibliographic record

VenueJournal of Settlements and Spatial Planning · 2017
Typearticle
Languageen
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsUniversité de MontréalUniversity of Guelph
FundersGouvernement Wallon
KeywordsUnintended consequencesPsychological interventionGovernment (linguistics)Vulnerability (computing)PopulationHuman settlementPolitical scienceEnvironmental planningBusinessGeographySociologyPsychology

Abstract

fetched live from OpenAlex

Governments of all levels have been involved to various degrees in dealing with environmental, social and economic issues, e.g. the vulnerability of human activities, coastal communities and cities generalculture and industry. While many interventions have been aimed at improving the situation for the affected populations and their environments, it is also the case that many interventions have given rise to unintended

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.282
GPT teacher head0.382
Teacher spread0.101 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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