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Record W2076673356 · doi:10.3917/popu.1401.0029

Ethnic Mobility of Aboriginal Peoples in Canada Between the 2001 and 2006 Censuses

2014· article· fr· W2076673356 on OpenAlexaffabout
Éric Caron‐Malenfant, Simon Coulombe, Éric Guimond, Chantal Grondin, André Lebel

Bibliographic record

VenuePopulation (English Edition) · 2014
Typearticle
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsMinistry of Energy, Northern Development and MinesStatistics Canada
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Cet article présente les résultats d’une analyse de la mobilité ethnique intragénérationnelle des Autochtones au moyen d’une source de données qui permet, pour la première fois au Canada, une estimation directe du phénomène : l’appariement entre les recensements de la population de 2001 et 2006. La mobilité ethnique intragénérationnelle, en d’autres termes les changements de déclaration de l’identité autochtone au cours de la vie, a contribué de manière importante à l’accroissement des populations métisses et indiennes de l’Amérique du Nord vivant hors des réserves indiennes au cours des dernières décennies. Cependant, les estimations publiées jusqu’ici reposant toutes sur des mesures indirectes, elles ne permettaient qu’une connaissance limitée des caractéristiques qui y sont liées. L’analyse, descriptive puis multivariée, montre que les gains de population que connaissent les Autochtones en raison de la mobilité ethnique résultent en réalité de flux multidirectionnels liés à certaines caractéristiques clés, comme le fait d’avoir des origines ethniques mixtes. L’article explore également les effets de la mobilité ethnique sur la composition sociodémographique des populations autochtones, leur répartition géographique étant notamment modifiée suite aux changements qui touchent la déclaration de l’identité aux recensements.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.257
Teacher spread0.241 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations26
Published2014
Admission routes2
Has abstractyes

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