THE DEVELOPMENT OF LANDESQUE CAPITAL IN THE MAYA LOWLANDS DURING THE MIDDLE PRECLASSIC
Bibliographic record
Abstract
ABSTRACT In this article, we argue that landesque capital was integral to the development of complexity in the Maya Lowlands. Such features involved permanent investments in the landscape that supported material and ideological practices, resulting in increased sustainability and well-being. We contend that these developments stemmed from accretional modifications to soils in the Preceramic/Early Preclassic, as well as intentional investments of labor in agricultural features, large public works, and select civic complexes during the Middle Preclassic. Capital improvements were particularly important during the Middle Preclassic, when sedentary occupations and civic life were established. The timing and location of the investments strongly correlate with other aspects of Middle Preclassic lifeways, such as the transition to sedentism, acquisition and control of resources, changes in lithic production, and the emergence of an elite class. We note that some of the largest investments in landscape management during the Middle Preclassic occurred in the Central Karstic Uplands, where substantial cities rose in the Late Preclassic. We conclude that during the Middle Preclassic an ontology of landesque capital developed, based on the synergistic fusing of daily and ritual practices with physical features, which provided a foundation for resilience, sustainability, and well-being in subsequent generations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".