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Record W2140093196 · doi:10.1177/0096144214566955

Suburban Land Development in Antebellum Boston

2015· article· en· W2140093196 on OpenAlexaboutno aff
Ronald Karr

Bibliographic record

VenueJournal of Urban History · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsnot available
Fundersnot available
KeywordsSuburbanizationReal estateRestructuringCommodityCapitalismQuarter (Canadian coin)AgricultureLand useReal estate developmentEconomyAgricultural economicsEconomic growthBusinessGeographyEconomicsMetropolitan areaMarket economyPolitical scienceArchaeologyFinanceCivil engineeringLaw

Abstract

fetched live from OpenAlex

Historians generally agree that American suburbanization began in the first quarter of the nineteenth century, brought on by a combination of technological innovation (particularly in transportation), economic restructuring, and changing tastes of the urban middle classes. Essential to this process was the rise of a suburban real estate industry, which converted agricultural land into a consumer commodity. Using a microhistorical study of a single farm in the Boston suburb of Brookline, Massachusetts, the origins of the process of suburban land development are examined from 1770 to 1850. Land records, local history, and genealogical information were used to reconstruct the process by which farm land became the site of suburban neighborhoods. Before 1840, when nearly all land transactions were between individuals who knew each other personally, there was no infrastructure of real estate professionals and developers to facilitate suburban growth. During the 1840s, sales between strangers, facilitated by agents or auctioneers, quickly became the norm, and residential subdivisions aimed at varying income levels appeared, accompanied by developers and builders. Land went from being the essential component of family farming to a general commodity freely bought and sold, part of the broader evolution of the American economy into one dominated by market-based industrial and financial capitalism.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.184
Teacher spread0.167 · 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 designNot applicable
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

Citations4
Published2015
Admission routes1
Has abstractyes

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