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Record W2019235041 · doi:10.7202/1025326ar

Transmission à rebours, filiation inversée, socialisation ascendante : regards renversés sur les rapports de générations

2014· article· fr· W2019235041 on OpenAlexaffvenue
Delphine Lobet, Lídia Eugênia Cavalcante

Bibliographic record

VenueEnfances Familles Générations · 2014
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Policies and Family
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Cet article introductif propose un aperçu de la situation présente de la recherche en sciences sociales sur la question des rapports de génération étudiés dans le sens inverse au sens habituel, c’est-à-dire dans le sens enfants-parents. Comment la recherche traite-t-elle cette importante question de la transmission à rebours, de la socialisation inversée, de la filiation ascendante, en bref comment traite-t-elle cette question : que font les enfants à leurs parents et à leur famille? Il propose ensuite de réfléchir au contexte actuel de l’accélération des savoirs : quel impact la société de l’information multiconnectée peut-elle avoir sur les rapports parents-enfants en ce qui concerne la transmission et la socialisation, précisément? Enfin, nous reviendrons sur les « trouvailles », sur les principales découvertes que les auteurs de ce numéro d’Enfances Familles Générations ont retirées de l’exercice de renversement du regard auquel ils se sont prêtés.

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)0.009
metaresearch head score (Gemma)0.019
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.012
Scholarly communication0.0060.008
Open science0.0010.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.030
GPT teacher head0.296
Teacher spread0.266 · 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

Citations8
Published2014
Admission routes2
Has abstractyes

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