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Record W2760700666 · doi:10.7202/1039358ar

Inégalités ethniques, disparités socioculturelles et hiérarchie de la terre à Hawkesbury et à Alfred en 1871

2017· article· fr· W2760700666 on OpenAlexaffvenueabout
Fernand Ouellet

Bibliographic record

VenueCahiers Charlevoix Études franco-ontariennes · 2017
Typearticle
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsYork University
Fundersnot available
KeywordsHumanitiesPolitical scienceArtGeography

Abstract

fetched live from OpenAlex

Fernand Ouellet poursuit son étude du profil socio-économique des communautés de langue française de l’est du Canada avant 1911. Utilisant la méthode comparative, il a jusqu’ici mis en rapport, entre elles et avec les autres populations de leurs provinces et régions, ces diverses collectivités à des points de vue divers – la démographie, l’agriculture, l’urbanisation, l’industrialisation, l’alphabétisation et la scolarisation – ; il a montré que leur appartenance religieuse et linguistique constituent des facteurs de distinction et qu’elles forment partout des communautés désavantagées. Le présent article lui permet de tester la validité de ses conclusions sur la communauté française du comté de Prescott, celles des cantons de Hawkesbury-Est, de Hawkesbury-Ouest et d’Alfred, à partir du recensement nominatif de 1871. Il commente les thèses courantes, certaines fantaisistes, sur l’estimation du nombre des migrants canadiens-français et les motivations qui les poussent à migrer, montrant que les considérations socio-économiques devancent de loin le besoin de se reproduire. Il explique enfin les inégalités ethniques des Franco-Ontariens par la hiérarchie de la terre, leur arrivée tardive justifiant la moindre étendue de leurs possessions foncières et les rendant disponibles pour le travail forestier.

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: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.123

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 studies0.0060.003
Scholarly communication0.0020.000
Open science0.0010.001
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 head0.017
GPT teacher head0.287
Teacher spread0.270 · 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

Citations0
Published2017
Admission routes3
Has abstractyes

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