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Record W4366112863 · doi:10.7202/1098250ar

Examen critique d’une projection démographique de Charles Gaudreault : Charles Gaudreault, « The impact of immigration on local ethnic groups’ demographic representativeness: The case study of ethnic French Canadians in Quebec », Nations and Nationalism, vol. 26, 4, 2020, p. 923-942.

2023· article· fr· W4366112863 on OpenAlexvenueaboutno aff
Michel Paillé

Bibliographic record

VenueRecherches sociographiques · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationEthnic groupHumanitiesPopulationRepresentativeness heuristicPolitical scienceDemographySociologyEthnologyArtAnthropologyPsychology

Abstract

fetched live from OpenAlex

À la suite d’une projection de l’évolution de la population québécoise d’origine ethnique française couvrant huit décennies (1971-2050), l’auteur examine plus particulièrement les résultats obtenus par M. Charles Gaudreault pour la période rétrospective 1971-2014. À partir de ce que nous connaissons sur notre passé démographique récent, concernant notamment l’immigration internationale et la fécondité, il s’avère que Charles Gaudreault a nettement surestimé (de près de 40 %) la croissance de la population immigrée, de ses enfants et de ses descendants, et sous-estimé (de près de 7 %) la majorité d’origine ethnique française. Mystifié par une « loi de puissance » qui ne tient nul compte de la structure par âge de la population, M. Gaudreault a cru faire la démonstration que le déclin des Canadiens français ne s’expliquerait que par une « immigration de masse ». À l’aide des projections démographiques de l’Institut de la statistique du Québec, l’auteur de cette note critique démontre qu’il n’en est rien, bien que par ailleurs notre fécondité demeure insuffisante pour assurer le remplacement des générations.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.011
Science and technology studies0.0010.004
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.083
GPT teacher head0.381
Teacher spread0.298 · 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 designQualitative
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

Citations2
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueRecherches sociographiquesSame topicCanadian Identity and HistoryFrench-language works237,207