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Record W2128259578 · doi:10.3917/popu.702.0221

Surviving Old Age in an Ageing World Old People in France, 1820-1940

2007· article· fr· W2128259578 on OpenAlex
Jérôme Bourdieu, Lionel Kesztenbaum

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePopulation (English Edition) · 2007
Typearticle
Languagefr
FieldHealth Professions
TopicAging, Elder Care, and Social Issues
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

Résumé Comparé aux autres pays européens, le vieillissement de la population a été, en France, particulièrement précoce. Cette évolution affecte aussi bien l’organisation de la société que les liens au sein de la famille. Si l’on observe l’évolution du patrimoine des Français entre 1820 et 1940, on constate que la part de ceux qui meurent sans rien laisser derrière eux augmente, et cette évolution s’observe à tous les âges. Partant de ce double constat, on cherche à analyser les stratégies que déploient les individus âgés pour vivre leur vieillesse, autour de trois types de ressources : les ressources économiques individuelles, les ressources familiales et les ressources publiques. L’analyse montre l’hétérogénéité du groupe des vieux et donc des stratégies de survie mises en œuvre. L’épargne n’est une solution que pour une minorité ; elle permet l’accès à d’autres ressources et constitue notamment une alternative au maintien d’une activité. On montre en outre que les retraites qui se mettent progressivement en place contribuent à un accès plus large à l’épargne. On observe finalement que la part croissante des personnes âgées dans la population française a été accompagnée par un rôle accru des soutiens publics.

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.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0010.002
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 head0.028
GPT teacher head0.346
Teacher spread0.319 · 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