MétaCan
Menu
Back to cohort
Record W2123137559 · doi:10.7202/029644ar

La contribution des Acadiens au peuplement des régions du Québec1

2009· article· fr· W2123137559 on OpenAlexaffvenueabout
Josée Bergeron, Hélène Vézina, Louis Houde, Marc Adélard Tremblay

Bibliographic record

VenueCahiers québécois de démographie · 2009
Typearticle
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsUniversité du Québec à Trois-RivièresUniversité du Québec à ChicoutimiUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Les Acadiens sont des descendants d’immigrants français qui se sont établis principalement au xviie siècle en Nouvelle-Écosse et au Nouveau-Brunswick. En 1755, les autorités britanniques ont ordonné la déportation des Acadiens qui ont été dispersés dans les colonies anglaises d’Amérique, en France et en Angleterre. On estime que de 2 000 à 4 000 Acadiens se sont établis au Québec. L’objectif de cette étude est de mesurer et de caractériser l’impact de l’apport migratoire acadien sur le pool génique québécois contemporain. Les données utilisées proviennent d’un corpus généalogique comprenant 2 340 ascendances. Les lieux d’origine des ancêtres, la fréquence de leurs mentions dans les généalogies ainsi que leur contribution génétique aux différentes populations régionales du Québec ont été analysés. Les résultats révèlent que de 46 % à 100 % des ascendances, selon la région, comprennent au moins un ancêtre d’origine acadienne. La contribution des fondateurs acadiens est particulièrement élevée aux Îles-de-la-Madeleine, où 86 % du pool génique leur est attribuable. Les populations de la Gaspésie (27 %) et de la Côte-Nord (14 %), affichent aussi une importante contribution acadienne.

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)0.002
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 score0.050
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.008
GPT teacher head0.231
Teacher spread0.223 · 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

Citations15
Published2009
Admission routes3
Has abstractyes

Explore more

Same venueCahiers québécois de démographieSame topicCanadian Identity and HistoryFrench-language works237,207