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Record W2149552343 · doi:10.7202/1020691ar

A Malthusian-Frontier Interpretation of United States Demographic History Before c. 1815

2013· article· en· W2149552343 on OpenAlexvenueno aff
Daniel Scott Smith

Bibliographic record

VenueUrban History Review · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFrontierUrbanizationElitePacePoliticsDevelopment economicsPopulationPopulation growthEconomicsInequalityPer capitaDemographic transitionInterpretation (philosophy)Economic geographyDemographic economicsEconomic historyGeographyPolitical scienceEconomic growthDemographySociologyLaw

Abstract

fetched live from OpenAlex

The combination of a three per cent rate of population growth and an absence of per capita economic growth was fundamental to the history of the British colonies in North America and the early United States. These characteristics sharply differed from the economy and demography of the nineteenth century United States and from the experience of other societies. These distinctive features had significant consequences; the "Malthusian-frontier" regime helps to explain the extremely slow pace of urbanization, the stability in the inequality of wealth, and the pattern of conflict and elite domination in politics. Although rapid natural increase created economic, social, and political difficulties, migration toward the frontier served to equilibrate the system. Using data from late eighteenth century New England towns, the paper demonstrates how migration tended to act as a homeostatic mechanism but also argues that out-migrants from more densely-settled areas were pushed rather than pulled. Several factors account for the "stickiness" of the migration process. Throughout, the essay illustrates the utility of a systemic approach to demographic history.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.001
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 head0.029
GPT teacher head0.197
Teacher spread0.168 · 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 designTheoretical or conceptual
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

Citations11
Published2013
Admission routes1
Has abstractyes

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