Toward multiscalar measures of inequality in archaeology
Bibliographic record
Abstract
The Gini coefficient is a statistical measure commonly used to characterize distributions of socioeconomic quantities. Archaeologists and social scientists have recently adopted this method to analyze ancient inequality by targeting specific proxy variables (e.g., residential unit size, burial data, etc.). Variations in the Gini are then examined in relation to key factors such as time, geography, and subsistence. Yet, Gini coefficients could be obtained across different scales of aggregation, from small neighborhoods within a larger settlement to an ensemble of multiple settlements that are part of the same polity. These different scales of aggregation represent considerable methodological and theoretical challenges, as larger scales might, for example, imply greater social and economic variation within groups and thus affect the Gini coefficients. Furthermore, these issues can also be exacerbated by the idiosyncrasies and limitations of historical and archaeological datasets. This paper discusses the potential and challenges of measuring Gini coefficients at and above the scale of individual archaeological sites, contrasting different approaches and discussing how each can reveal insights into different patterns of past wealth inequality, addressing methodological, empirical, and theoretical implications arising from the multiscalar nature of human interactions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".