11 Resilience and Vulnerability in the Maya Hinterlands
Bibliographic record
Abstract
ABSTRACT Given their grand architecture, intricately carved monuments, and colorful histories, the largest Maya centers have long drawn the attention of archaeologists and non‐specialists alike. Early interest in the infamous Maya collapse was, in fact, initially inspired by the discovery of these “lost cities in the jungle.” This research focus was further stimulated by advances in deciphering the Maya hieroglyphic script, and the recognition that monument erection—or in other words, the written histories of most of the southern Lowland centers—came to a rather abrupt end in the 9th century C.E. IHOPE scholars are attempting to elucidate the conditions that lead to the decline of these impressive centers. In doing so, the trajectories of smaller communities, and or those located in hinterlands between the more prominent centers, have emerged as interesting counterpoints that provide unique, and no less significant, examples of resilience and vulnerability. The emerging data suggest that these communities had specific strengths and weaknesses, which in turn provided them with a particular set of challenges, as well as a specific range of coping mechanisms they could marshal when dealing with their ever‐changing environment circumstances (i.e., climate change, resource availability, landscape modifications), and the highly dynamic geopolitical landscape within which they were embedded. This chapter will discuss some of the key insights derived from our examination of hinterland communities, with particular attention being paid to the broader implications of the contrasting trajectories exhibited by these segments of ancient Maya society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".