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Record W2904262812 · doi:10.3138/anth.2017-0006-fr

Introduction: Ethnographie, performance et imagination

2018· article· fr· W2904262812 on OpenAlexaffvenue
Magdalena Kazubowski‐Houston, Virginie Magnat

Bibliographic record

VenueAnthropologica · 2018
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaYork University
Fundersnot available
KeywordsHumanitiesEthnographySociologyArtAnthropology

Abstract

fetched live from OpenAlex

Cette Introduction à la section thématique « Ethnographie, performance et imagination » explore la performance comme « ethnographie imaginative » (Elliott et Culhane 2017), c’est-à-dire comme pratique de recherche transdisciplinaire, collaborative, incarnée, critique et engagée qui s’inspire de l’anthropologie et des arts créatifs. L’accent est mis en particulier sur la performativité de la performance (un événement délibérément mis en scène pour un public), employée à la fois comme processus ethnographique (travail de terrain) et comme mode de représentation ethnographique. Les questions posées sont les suivantes: La performance peut-elle nous aider à étudier et à mieux comprendre les mondes de la vie imaginative tels qu’ils se déploient dans le moment présent? La performance nous permet-elle de repenser l’anthropologie de l’imagination? Est également posée la question de savoir si le travail mené à la croisée de l’anthropologie, de l’ethnographie, de la performance et de l’imagination permet de transformer la façon dont sont abordés les processus et les produits ethnographiques, les questions de réflexivité et de représentation, les relations ethnographes-participants et les publics ethnographiques. La manière dont la performance employée comme ethnographie peut nous aider à reconceptualiser l’engagement public et l’activisme ethnographique, l’ethnographie collaborative/participante, ainsi que la recherche interdisciplinaire au sein et au-delà du monde universitaire, est aussi examinée. Enfin, cette Introduction donne un bref aperçu des contributions à cette section thématique, lesquelles abordent ces questions de différents points de vue théoriques, méthodologiques et thématiques.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.790
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.019
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.206
GPT teacher head0.501
Teacher spread0.295 · 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; both teacher heads agree on what is shown here.

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 routes2
Has abstractyes

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