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Record W2325313660 · doi:10.7183/0002-7316.79.4.67

Labor Costs for Prehistoric Earthwork Construction: Experimental and Archaeological Insights from the Lower Yangzi Basin, China

2015· article· en· W2325313660 on OpenAlexaff
Liye Xie, Steven L. Kuhn, Guoping Sun, John W. Olsen, Yunfei Zheng, Ding Pin, Ye Zhao

Bibliographic record

VenueAmerican Antiquity · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsUniversity of Toronto
FundersAmerican Council of Learned Societies
KeywordsEarthworksPrehistoryDiggingArchaeologyStructural basinChinaScale (ratio)GeographyEngineeringGeologyGeotechnical engineeringPaleontologyCartography

Abstract

fetched live from OpenAlex

Abstract This paper examines choices of earth-working tools made by Neolithic Chinese populations. In the Hemudu Culture (7000–5000 B.P.), bone (scapula) digging tools were used from the earliest times, whereas peoples in surrounding areas used stone spades. A range of experiments on manufacturing costs, durability, and use efficiency under realistic conditions show that bone and stone spades are functionally equivalent when soils are soft, but that stone implements provide significant and easily perceived advantages when working harder soils. The persistence of scapular spades in the Hemudu Culture would have constrained decisions about undertaking large construction projects under normal soil conditions. Our results show that, in addition to generalized labor for construction, labor demands for producing earth-working implements for large-scale prehistoric earthworks could have also been substantial. These findings not only help explain the processes of intensifying rice-agriculture and sedentary settlements in the Lower Yangzi Basin, but also create a solid foundation for further investigation of how the recruitment of both generalized and specialized laborers, the organization of craft production, and the relevant logistics for large-scale earthworks may have paralleled concentrations of political power in prehistory.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score1.000

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 studies0.0000.003
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 head0.014
GPT teacher head0.224
Teacher spread0.210 · 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

Citations18
Published2015
Admission routes1
Has abstractyes

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