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Record W4402794148 · doi:10.7202/1113541ar

Savoir-faire des viticulteurs francophones de la péninsule du Niagara

2023· article· fr· W4402794148 on OpenAlexaffvenue
Hélène Pomarès

Bibliographic record

VenueEthnologies · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsArtPolitical science

Abstract

fetched live from OpenAlex

Depuis des décennies, plusieurs viticulteurs francophones ont choisi de s’installer dans la région de Niagara. En effet, cette région bénéficie d’un microclimat propice à la culture de la vigne. Ces viticulteurs passionnés constituent une richesse humaine et multiculturelle d’une grande valeur patrimoniale. Or, très peu d’études ont été publiées concernant leur capacité d’adaptation et de transmission de leurs savoir-faire en milieu minoritaire. Pourtant, ces professionnels francophones ont dû surmonter plusieurs défis afin de pouvoir transmettre leur savoir-faire. Qu’ont-ils fait concrètement en termes d’adaptation et de passation des acquis? Les objectifs de cette recherche de terrain visent donc à : 1) recenser et analyser plusieurs retours d’expérience au moyen d’entrevues semi-dirigées 2) apporter des pistes de solution que pourraient adopter la relève. Ceci permet de comprendre les enjeux et les défis qu’ont dû relever ces viticulteurs francophones afin d’assurer la pérennisation de leur savoir-faire pour les années à venir.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science 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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.009
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.049
GPT teacher head0.295
Teacher spread0.245 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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