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Record W2981074518 · doi:10.1073/pnas.1906196116

Work time and market integration in the original affluent society

2019· article· en· W2981074518 on OpenAlexaff
Rahul Bhui, Maciej Chudek, Joseph Henrich

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsCanadian Institute for Advanced Research
Fundersnot available
KeywordsSubsistence agricultureSocioeconomic statusWork (physics)Time allocationDemographic economicsScale (ratio)EconomicsDevelopment economicsEconomic growthGeographySociologyDemographySocial science

Abstract

fetched live from OpenAlex

Does integration into commercial markets lead people to work longer hours? Does this mean that people in more subsistence-oriented societies work less compared to those in more market-integrated societies? Despite their venerable status in both anthropology and economic history, these questions have been difficult to address due to a dearth of appropriate data. Here, we tackle the issue by combining high-quality time allocation datasets from 8 small-scale populations around the world (45,019 observations of 863 adults) with similar aggregate data from 14 industrialized (Organisation for Economic Co-operation and Development) countries. Both within and across societies, we find evidence of a positive correlation between work time and market engagement for men, although not for women. Shifting to fully commercial labor is associated with an increase in men's work from around 45 h per week to 55 h, on average; women's work remains at nearly 55 h per week across the spectrum. These results inform us about the socioeconomic determinants of time allocation across a wider range of human societies.

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 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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.383
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.299
Teacher spread0.274 · 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 teacher head, 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

Citations29
Published2019
Admission routes1
Has abstractyes

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