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Record W2943403788 · doi:10.1017/aaq.2018.93

A Quantitative Dwelling-Scale Approach to the Social Implications of Maize Horticulture in New England

2019· article· en· W2943403788 on OpenAlexaff
William A. Farley, Amy Fox, Gabriel Hrynick

Bibliographic record

VenueAmerican Antiquity · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsUniversity of New BrunswickUniversity of Toronto
Fundersnot available
KeywordsWoodlandPeninsulaGeographySettlement (finance)RestructuringScale (ratio)ArchaeologyEcologyPolitical scienceBiologyCartography

Abstract

fetched live from OpenAlex

We compare domestic architectural features in New England and the Maritime Peninsula to investigate the relationship between the adoption of horticulture and its relationship to social and settlement change during the Woodland Period. Horticulture was not practiced on the Maritime Peninsula until after European contact, despite cultural and environmental similarity to New England. In New England, horticulture has been implicated in profound social and settlement changes. However, aggregated villages, a unit typically investigated for evidence of social change, have proven elusive in the archaeological record. We compiled and analyzed a dataset of dwelling features instead of relying on identifiable villages. This novel quantitative approach uses dwelling feature shape and size as a proxy for social and settlement change, considering these changes at the scale of the house. We find that, during the Woodland Period, dwelling size was overall slightly larger in New England than on the Maritime Peninsula, but ranges heavily overlapped. After the introduction of horticulture, however, dwellings in New England grew in size overall and assumed bimodally distinct larger and smaller forms, which likely necessitated a restructuring of social and economic behavior. This pattern correlates maize horticulture with changes in social and economic lifestyle in Late Woodland New England.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.334

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.016
GPT teacher head0.252
Teacher spread0.236 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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