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Record W3043019375 · doi:10.46665/kwe.2018.12.19.3.193

Local Innovation for Public Transfers and Management Policy in the Case of Canada

2018· article· en· W3043019375 on OpenAlexaboutno aff
Silvia Amato

Bibliographic record

VenueKorea and the World Economy · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsNuclear powerDimension (graph theory)Public economicsEconomicsPublic policyRegional sciencePolitical scienceEconomic systemPublic administrationPolitical economySociologyEconomic growthEcology

Abstract

fetched live from OpenAlex

For the critical knowledge area of the nuclear industry and nuclear waste management what has come into evidence is the attachment to territorial and identity norms. Most studies have referred to environmental politics and environmental justice but, at the same time, it needs to be addressed the territorial national dimension. In connection with public and private partnerships (PPPs) in Canada, the sectoral development has been experienced in terms of public performances and territorial practices. For the management of nuclear power plants through the PPPs, political relationships have been shaped according to different levels of productive interactions, which have been related to environmental affectation policies. The comparative increase of regional productions and trans-sectoral economic interests has determined social adaptive patterns indicating the interrelated environmental justice issues, which have been expressed on the basis of common knowledge platforms and social open confrontations.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.140
Threshold uncertainty score0.998

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0200.011
Scholarly communication0.0110.004
Open science0.0020.006
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0240.001

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 head0.021
GPT teacher head0.278
Teacher spread0.257 · 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 designNot applicable
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

Citations0
Published2018
Admission routes1
Has abstractyes

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