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Record W2125664012 · doi:10.7202/044456ar

Québécoises et Ontariennes en voiture !

2010· article· fr· W2125664012 on OpenAlexaffvenueabout
Maude-Emmanuelle Lambert

Bibliographic record

VenueRevue d histoire de l Amérique française · 2010
Typearticle
Languagefr
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesArtPolitical science

Abstract

fetched live from OpenAlex

Cet article s’intéresse à l’émergence et au développement d’une culture de l’automobile au Québec et en Ontario et en particulier au rôle qu’y tiennent les femmes. Dès ses débuts, l’automobilisme est investi et organisé par les catégories culturelles de la masculinité et de la féminité. Par le biais de l’automobile, on perpétue les croyances et les valeurs associées à chacun des sexes, puisque c’est l’homme qui en détient le contrôle exclusif. On croit les femmes incapables de maîtriser la technologie ; elles n’auraient ni la force physique ni les connaissances et les nerfs nécessaires à la conduite de l’automobile. Or, selon les historiennes féministes, l’automobile a représenté pour les femmes un objet de pouvoir, un moyen de prendre cette autonomie qu’on leur refusait ; la femme automobiliste étant vue comme un symbole fort de l’égalité entre les sexes. Au-delà de la représentation symbolique de l’automobile dans l’histoire des femmes, que sait-on de l’expérience féminine de l’automobile ? Quels regards posaient-elles sur cette nouvelle expérience culturelle et spatiale ?

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0170.006
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0380.002

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 head0.008
GPT teacher head0.196
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2010
Admission routes3
Has abstractyes

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