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Record W3123375354 · doi:10.4054/mpidr-wp-2016-002

The consequences of sibling rivalry on survival and reproductive success across different ecological contexts: a comparison of the historical Krummhörn and Quebec populations

2016· preprint· en· W3123375354 on OpenAlexaffabout
Jonathan F. Fox, Kai P. Willführ, Alain Gagnon, Lisa Dillon, Eckart Voland

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsUniversité de Montréal
FundersDeutsche Forschungsgemeinschaft
KeywordsSibling rivalry (animals)SiblingRivalryEcologyGeographyDemographyBiologyPsychologySociologyDevelopmental psychologyEconomics

Abstract

fetched live from OpenAlex

This article investigates the relationship between large families and the probability of offspring survival, marriage, and fertility across the historical populations of the Quebec (1670-1799) and Krummhörn regions (1720-1874).Both populations exist in agriculturally based economies, but differ in important ways.The Krummhörn population faced a fixed supply of land, which was concentrated amongst a small number of farmers.Most individuals were landless agricultural workers who formed a relatively competitive labor supply for the large farmers.In contrast, individuals in Quebec had access to a large supply of land, but with far fewer available agricultural workers, had to rely on their family to develop and farm that land.Results indicate that more siblings of the same gender were generally associated with increases in mortality during infancy and childhood, later ages of first marriage, and fewer numbers of children ever born.For mortality and age at first marriage, the effects of sibling formation appear strongest in the Krummhörn region.This indicates that although sibship effects appear in both ecological contexts, that the context of the region mattered in determining their magnitude.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.141
GPT teacher head0.400
Teacher spread0.259 · 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 designObservational
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
Published2016
Admission routes2
Has abstractyes

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