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Record W4393259285 · doi:10.1017/s0956536123000202

Inequality on the southwest Maya frontier: House size variations in three polities of the Rosario Valley, Chiapas

2023· article· en· W4393259285 on OpenAlexaff
Kyle Shaw-Müller, John P. Walden

Bibliographic record

VenueAncient Mesoamerica · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLatin American Urban Studies
Canadian institutionsUniversity of Toronto
FundersUniversity of Pittsburgh
KeywordsMayaFrontierGeographyEthnologyArchaeologyHistory

Abstract

fetched live from OpenAlex

Abstract Being a form of labor investment, house size is frequently analyzed as an index of socioeconomic inequality. However, datasets that lack wide-ranging residential stratigraphic information are not reliable sources of labor investment estimates. This is the case for Late Classic domestic architecture data from three polities in the Rosario Valley (modern-day Chiapas) on the southwest Maya frontier: Rosario, Ojo de Agua, and Los Encuentros. Although the sample's house size inequality generally cannot index period-specific labor investment, it may signify prestige differentiation. For each polity we generated Lorenz curves and calculated Gini coefficients for five variables representing house size (area and volume). Results resemble inequality data from lowland Classic Maya centers. We also demonstrate that the smallest, shortest-lived polity had more equal house size values, likely due to the modesty of its apical elite architecture. In contrast, the two larger, older polities were more unequal because they had substantial palaces.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score0.312

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.041
GPT teacher head0.289
Teacher spread0.248 · 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 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

Citations5
Published2023
Admission routes1
Has abstractyes

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