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Record W2099652866 · doi:10.22004/ag.econ.273728

Property Rights, Standards of Living, and Economic Growth: Western Canadian Cree

2010· preprint· en· W2099652866 on OpenAlexaboutno aff
Ann M. Carlos, Frank D. Lewis

Bibliographic record

VenueAgEcon Search (University of Minnesota, USA) · 2010
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsnot available
FundersSmithsonian Institution
KeywordsStandard of livingGreat DivergenceDivergence (linguistics)Property rightsChinaWageGeographyPower (physics)Property (philosophy)Development economicsEconomicsEconomyPolitical scienceDemographic economicsLabour economicsMarket economyLawArchaeology

Abstract

fetched live from OpenAlex

The Great Divergence in standards of living for populations around the world occurred in the late 18th century. Prior to that date evidence suggests that real wages of most Europeans, many living in China and India were similar. Some a little higher and some a little lower but with a low dispersion. By the middle of the 19th century, a divergence had occurred with western Europe pulling away from other groups. Little is known about the standards of living of the aboriginal peoples of North America many of whom were primarily hunter/gatherer’s at the end of the 18th century. Based on comparisons of expenditure, we show that the standard of living of aboriginal people in 1740 was similar to that of wage workers in London. However, within the next century, there would be a great divergence. This paper explores the ways in which huntergatherer lifeways and the concomitant property rights structures reduced the likelihood that native economy could experience modern rates of economic growth. Native society and property rights structures which provided a relatively high standard of living in the mid eighteenth century and for part of the nineteenth was unable to provide avenues for further development.

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)0.001
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.034
GPT teacher head0.212
Teacher spread0.178 · 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.

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

Citations5
Published2010
Admission routes1
Has abstractyes

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