MétaCan
Menu
Back to cohort
Record W2742570999 · doi:10.1126/sciadv.1700497

Effects of population dispersal on regional signaling networks: An example from northern Iroquoia

2017· article· en· W2742570999 on OpenAlexaffabout
John P. Hart, Jennifer Birch, Christian Gates St-Pierre

Bibliographic record

VenueScience Advances · 2017
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsBiological dispersalPopulationGeographyEcologyBiologyComputer scienceMedicineEnvironmental health

Abstract

fetched live from OpenAlex

The dispersal of Iroquoian groups from St. Lawrence River valley during the 15th and 16th centuries A.D. has been a source of archaeological inquiry for decades. Social network analysis presented here indicates that sites from Jefferson County, New York at the head of the St. Lawrence River controlled interactions within regional social signaling networks during the 15th century A.D. Measures indicate that Jefferson County sites were in brokerage liaison positions between sites in New York and Ontario. In the network for the subsequent century, to which no Jefferson County sites are assigned, no single group took the place of Jefferson County in controlling network flow. The dispersal of Jefferson County populations effectively ended this brokerage function concomitant with the emergence of the nascent Huron-Wendat and Iroquois confederacies and may have contributed to the escalation of conflict between these entities. These results add to a growing literature on the use of network analyses with archaeological data and contribute new insights into processes of population relocation and geopolitical realignment, as well as the role of borderlands and frontiers in nonstate societies.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0080.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.052
GPT teacher head0.388
Teacher spread0.336 · 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 teacher head, not a consensus.

Study designObservational
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

Citations32
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueScience AdvancesSame topicIndigenous Studies and EcologyFrench-language works237,207