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Record W2969570746 · doi:10.7202/1062105ar

Les défis d’une nomenclature commune des professions pour l’étude de la mobilité intergénérationnelle en France et au Québec1

2019· article· fr· W2969570746 on OpenAlexaffvenueabout
Delphine Rémillon, Marianne Kempeneers, Éva Lelièvre

Bibliographic record

VenueCahiers québécois de démographie · 2019
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Policies and Family
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Il existe en France une longue tradition d’étude de la mobilité sociale, mais pendant longtemps les processus de transmission professionnelle au sein des familles n’ont été étudiés que de père en fils. Au Canada, ces analyses sont moins fréquentes, en raison du peu de données disponibles et parce que les nomenclatures se prêtent difficilement à un codage hiérarchique des professions. Tirant parti des enquêtes Biographies et entourage (INED) et Biographies et solidarités familiales au Québec (Université de Montréal), nous pouvons étudier les transmissions sur plusieurs générations de façon comparative entre la France et le Québec et identique pour les femmes et les hommes. Il a d’abord fallu coder les professions d’une manière comparable et adaptée à l’étude de la mobilité sociale. L’appui sur la nomenclature française, qui décrit les structures socioprofessionnelles de façon stable dans le temps, a permis de recréer des catégories hiérarchiques pour le Québec également. Le présent article rend compte de ces questions méthodologiques et du potentiel de ces deux enquêtes pour analyser la mobilité sociale.

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)0.028
metaresearch head score (Gemma)0.035
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 score0.112
Threshold uncertainty score0.474

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.011
Science and technology studies0.0170.021
Scholarly communication0.0090.004
Open science0.0030.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.011
GPT teacher head0.286
Teacher spread0.276 · 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
Published2019
Admission routes3
Has abstractyes

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