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Record W4210335519 · doi:10.51270/45.2.259

Strangers No More: Kinship, Clanship, and the Incorporation of Newcomers in Northern Iroquoia

2021· article· en· W4210335519 on OpenAlexvenueaboutno aff
Jonathan Micon, Jennifer Birch, Ronald F. Williamson, Louis Lesage

Bibliographic record

VenueCanadian Journal of Archaeology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical and Cultural Archaeology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsKinshipClanPopulationScale (ratio)GenealogyFifteenthStructuringSociologyEconomic geographyMovement (music)Social organizationGeographyAnthropologyEthnologyHistoryDemographyPolitical scienceAncient historyAestheticsCartographyLaw

Abstract

fetched live from OpenAlex

In this paper, we consider how institutions of kinship facilitated the integration of peoples originating in the St. Lawrence Valley into ancestral Huron-Wendat communities in the fifteenth and sixteenth centuries AD. We present some general principles regarding the role of kinship in structuring social relations, processes of population movement, and the integration of newcomers. Data on the distributions and frequencies of characteristic St. Lawrence Iroquoian artifacts on four ancestral Huron-Wendat village sites in Ontario, Canada are utilized to infer the scale of population movement and processes of incorporation into lineages and clan segments. We argue that interpretive frameworks that explicitly incorporate categories and institutions of relatedness with traditional material culture analyses can shed new light on how groups of newcomers of varying scale and composition were integrated into Huron-Wendat households and communities.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score0.989

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.020
GPT teacher head0.253
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2021
Admission routes2
Has abstractyes

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