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Record W4412804461 · doi:10.51363/unifr.lma.2025.020

Soutien à la parentalité comme outil d’intégration chez les familles issues de l’immigration

2025· dissertation· fr· W4412804461 on OpenAlexaboutno aff
Alexia Volery

Bibliographic record

Venuenot available
Typedissertation
Languagefr
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceImmigrationINTSociologyComputer science

Abstract

fetched live from OpenAlex

Ce travail explore la conception d’un soutien à la parentalité promouvant une intégration pour les familles issues de l’immigration. De fait, en plus d’assurer leur fonction éducative, les parents issus de l’immigration entrent dans un processus d’acculturation et voient des changements s’opérer dans leurs schémas culturels. Parmi les différentes manières d’aborder l’acculturation, la stratégie d’intégration s’avère être la plus favorable en termes de bien-être et d’adaptation. Cependant cette intégration n’est pas toujours rendue possible par les instances de soutien à la parentalité qui recourent parfois à des injonctions normatives, à des exigences d’assimilation ou à une approche parentaliste. Un soutien à la parentalité adapté peut cependant constituer un véritable outil d’intégration pour les familles issues de l’immigration, notamment en engendrant des discussions autour des différences de pratiques éducatives. Ce travail analyse la capacité d’un dispositif québécois et de plans d’action fribourgeois à répondre à ces différents enjeux pour offrir aux familles issues de l’immigration un soutien favorisant leur intégration. Bien que la plupart de ces programmes semblent promouvoir une intégration, cette analyse soulève que leur concrétisation en contexte fribourgeois reste à confirmer.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score0.576

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.007
Scholarly communication0.0060.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.069
GPT teacher head0.377
Teacher spread0.308 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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