A Revised Chronology for the Emergence and Expansion of Late Woodland Villages along the North Shores of Lake Erie and Lake Ontario and Evidence for a Rapid Increase in Fortified Settlements in the Thirteenth Century AD
Bibliographic record
Abstract
In this paper, we present a revised chronology for the appearance and development of village communities dating to the first part of the Late Woodland across the north shores of Lake Erie and Lake Ontario (Ontario, Canada). Our work is based on a sample of existing and newly obtained accelerator mass spectrometry (AMS) dates from Late Woodland sites dating before AD 1450. We have examined these within a Bayesian modelling framework to provide a more precise understanding of the timing and pace of cultural change, with a focus on the changes in settlement size and organization structure. Our results emphasize the longevity and adaptive success of low-level food production among communities along the Grand River in the first phase of the Late Woodland. We also show that the transition to palisaded villages and fortified towns was not a slow four-century-long process that conventional dating implied. Instead, these changes unfolded over 150 years, exhibiting a more rapid transition than has previously been recognized, concentrated in the thirteenth century AD. These results are interpreted within the context of the growing value of intra-community cohesion alongside evidence for inter-community conflict.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".