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Record W2692602890

Locals in the Wasteland: The Non-Intended Side Effects of (In)Sensible Local Participatory Technology Development.

2017· article· en· W2692602890 on OpenAlexaboutno aff
Céline Parotte

Bibliographic record

VenueOpen Repository and Bibliography (University of Liège) · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsnot available
Fundersnot available
KeywordsCitizen journalismParticipatory developmentBusinessEnvironmental planningPolitical scienceGeography
DOInot available

Abstract

fetched live from OpenAlex

Implementing high-level radioactive wastes programs on a territory remain a sensitive step for nuclear waste management organizations. At this stage, the preferred solution of radioactive wastes programs (geological disposal) and their technological developments become highly visible and this is why the new instruments and strategies such as participatory technology development (PDT) have been adopted to tackle the territorial development of geological disposal technology. Considering the coproduction between territory and program (Jasanoff 2004), this presentation focuses on two siting processes of radioactive wastes programs, in France and in Canada to analyze the influence of in(sensible) local participatory technology development strategy and the influence of invited critics (Wynne 2007) on the industrial project. Our results show that frameworks of the participatory technology development have been designed very differently (with or without publics). Consequently, two undesirable, mostly non-intended side effects of information local committees (non) integration have appeared. In France, a closed PDT transformed invited critics as non-invited one. In Canada, a weak engagement of publics has occurred with an opened PDT. In both case, new socio-technical adjustments have occurred in those programs.

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 score0.019
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.024
GPT teacher head0.237
Teacher spread0.213 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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