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Record W2754705787 · doi:10.7202/1050492ar

Les services écosystémiques en ville, ou l’opportunité d’une réconciliation entre les humains et la nature

2018· article· fr· W2754705787 on OpenAlexvenueno aff
Lucas Brunet

Bibliographic record

VenueEnvironnement urbain · 2018
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsConciliationContext (archaeology)Political scienceHumanitiesGeographyWelfare economicsSociologyArtEconomicsMediation

Abstract

fetched live from OpenAlex

Cet article propose de comprendre comment la notion de services écosystémiques fonctionne de manière affective en milieu urbain. Les affects apparaissent ciblés et façonnés par un pouvoir qui cherche à rendre la présence de la nature en ville attractive et désirable. Ils contribuent à produire une nature et des sujets singuliers. À partir d’une étude de cas portant sur la Mission Économie de la Biodiversité et sur deux entreprises bailleuses de foncier, l’article montre que l’action du dispositif des services écosystémiques s’accompagne d’une réconciliation entre l’humain et la nature, d’une promesse de bien-être et d’un sens de l’opportunisme. This article aims to find out how the notion of ecosystem services operates affectively in an urban context. Affects appear to be targeted and shaped by powers that seek to render the presence of nature attractive and desirable in cities. They produce a singular nature and specific subjects. Drawing on a case study of an organization bringing together economy and biodiversity and two lessor companies, the article highlights that the actions of the device of ecosystem services are accompanied by a reconciliation between humans and nature, a promise of well-being and a sense of opportunism.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.027
GPT teacher head0.291
Teacher spread0.263 · 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.

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

Citations1
Published2018
Admission routes1
Has abstractyes

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