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Record W3082655790 · doi:10.4000/pa.1181

Comment saisir et comprendre la marche en ville ?

2020· article· fr· W3082655790 on OpenAlexaff
Denis Cerclet, Thierry Boissière, Mouloud Boukala, Liliane Buccianti Barakat, Spyros Franguiadakis, Annie Tohme-Tabet, Rita Zaarour

Bibliographic record

VenueParcours anthropologiques · 2020
Typearticle
Languagefr
FieldSocial Sciences
TopicMiddle East Politics and Society
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

À partir d’une recherche menée dans deux rues proches du centre-ville de Beyrouth, une équipe pluridisciplinaire composée d’anthropologues, de géographes et de sociologues, s’interroge sur la complexité de la marche dans une ville considérée comme chaotique et saturée par la circulation automobile. Dans la perspective des approches relationnistes et afin de comprendre comment s’établit une écologie de la perception, une méthodologie fondée sur la captation du regard et du cheminement avec un eye tracker (oculomètre) est mise en œuvre. Cet instrument permet, entre autres, d’envisager la dimension du « corps en acte », y compris dans l’explicitation par les personnes participantes des matériaux visuels et sonores recueillis.

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 score0.911
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 studies0.0010.005
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.056
GPT teacher head0.373
Teacher spread0.317 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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