Beneath the surface, below the line: Exploring household differentiation at Las Cuevas using Gini coefficients
Bibliographic record
Abstract
Abstract During the Late Classic period (a.d. 550–900), ancient Maya settlement spread throughout western Belize, including the Vaca Plateau, a rugged karstic region with high densities of ritually utilized cave systems. Within the past decade, archaeologists have increasingly drawn on LiDAR technology to document the extent of such settlement at local and regional scales. Combined with traditional pedestrian survey, we have begun to amass substantial data on variation within household groups, disparities which may indicate inequality within these communities. Here, we use settlement data generated from the Las Cuevas region to quantify residential variation through Gini coefficients and Lorenz curves. Special attention is given to areal and volumetric deviation of identified households within three samples: (1) the complete 95.25 km2 study area; (2) a 12.25 km2 zone of higher population between the primary centers of Las Cuevas and Monkey Tail; and (3) households situated within 500 m of ritually utilized caves within the study area. Results indicate some degree of variation within household area and volume for all samples, suggestive of unequal access to labor within the region. This research adds to the growing database of Gini-based analyses to improve our understanding of wealth differentials within pre-modern populations throughout the Lowlands.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".