Territorial Expansion in the Viru State: Updating Old Settlement Patterns to Explore New Ideas
Bibliographic record
Abstract
The territorial-expansion model, recently proposed as a general model to explain the development of first-generation states, is tested in the Virú Valley of Peru. The Virú state developed around 200 BC and is the earliest known state on the north coast of Peru. The settlement patterns and settlement hierarchies of the Virú Period (ca. 200 BC – 600 AD) are compared with those of the earlier Puerto Morin Period (ca. 400 – 200 BC) to investigate processes of territorial expansion. Two independent polities and several outlying communities occupied the valley during the Puerto Morin Period whereas settlement during the Virú Period was extensive, populations surged, and large swaths of the valley were settled for the first time. Evidence is presented to show that the Virú state incorporated the earlier Puerto Morin polities and that a three-tiered settlement hierarchy existed at this time. Drawing from cross-cultural evidence and recent studies on Virú expansionary dynamics, I propose a hypothesis that the Virú state expanded rapidly throughout the valley early in its developmental history and pursued a policy of territorial consolidation later in its history. Ultimately, the Virú case supports the territorial-expansion model of early state development.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".