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Record W4319659769 · doi:10.1017/9781108878142.014

Mesopotamian City-States

2023· book-chapter· en· W4319659769 on OpenAlexaff

Bibliographic record

VenueCambridge University Press eBooks · 2023
Typebook-chapter
Languageen
FieldSocial Sciences
TopicEurasian Exchange Networks
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPeriod (music)MesopotamiaNarrativeEliteWage labourWageGeographyEconomyAgricultureEconomic historyEconomic geographyHistoryPolitical scienceEconomicsArchaeologyLawPolitics

Abstract

fetched live from OpenAlex

After an introductory section that frames some conceptual issues surrounding the emergence of city-states, most of the chapter is devoted to a chronological narrative describing the case of southern Mesopotamia. This includes sections on the pre-’Ubaid period, the ’Ubaid period, the Uruk period, and the post-Uruk period. The key puzzle is how to explain the transition from scattered villages and small towns in the ’Ubaid period to large city-states with tens of thousands of residents in the Uruk period. Following the main narrative, we review causal hypotheses on this subject proposed by archaeologists and economists. These include ideas about climate change, migration, food production, manufacturing, trade, warfare, and culture. We also offer a synthesis of our own. In our view, the prime mover was increasing aridity, which motivated migration from outlying areas toward the south. As this process unfolded, commoner living standards fell, which enabled local elites in the south to employ commoners at a lower wage. When the wage had fallen far enough, urban manufacturing became profitable. Elite taxation of urban manufacturing was probably easier than taxation of rural agriculture, and this provided the fiscal foundations for early city-states like Uruk.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.777
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.045
GPT teacher head0.244
Teacher spread0.200 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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