Coalescent Communities: Settlement Aggregation and Social Integration in Iroquoian Ontario
Bibliographic record
Abstract
Abstract This paper explores processes of settlement aggregation among ancestral Huron-Wendat populations in south-central Ontario, Canada. During the fifteenth century A.D., numerous small communities came together, forming large, fortified village aggregates. In order to understand these processes a multiscalar analytical approach was combined with a conceptual framework emphasizing cross-cultural perspectives on coalescent societies, the archaeology of communities, and historical trajectories of societal change. Regional settlement data are presented to illustrate the movement and increasing size of settlements. In order to determine how individual coalescent communities were formed and maintained, a single village relocation sequence is examined in detail. This sequence illustrates how people constructed, inhabited, and negotiated domestic and public spaces in these new community aggregates. Detailed analyses of the occupational histories of these sites point to the creation of new community-based identities, corporate decision-making structures, and increasing social integration over time. The results of this study demonstrate that while settlement aggregation can be documented at the regional level, only detailed intrasite analyses can identify the small-scale changes in practice that reflect the lived experience of coalescence.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".