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Life after Lapita

2024· book-chapter· en· W4392082423 on OpenAlexaff
Ben Shaw, Sean P. Connaughton

Bibliographic record

VenueOxford University Press eBooks · 2024
Typebook-chapter
Languageen
FieldSocial Sciences
TopicPacific and Southeast Asian Studies
Canadian institutionsKwantlen Polytechnic University
Fundersnot available
KeywordsSubsistence agricultureSettlement (finance)PotteryGeographyNew guineaHuman settlementArchaeologyEthnologySocial complexityPopulationPrehistoryMosaicDiversity (politics)HistoryAnthropologySociologyDemographyAgriculture

Abstract

fetched live from OpenAlex

Abstract The spread of Lapita cultural groups through the New Guinea region 3350–3250 years ago and into the uninhabited remote Pacific islands from 3050–3000 years ago was one of the greatest migrations in human history. Over subsequent millennia, novel adaptations and intergenerational social linkages led to the emergence of a complex mosaic of culture and language reflected in modern Pacific populations. Broadly defined as “post-Lapita,” this chapter assesses the current breadth of archaeological information about human settlement and practices in the few centuries after the arrival of Lapita communities within the former ambit of their distribution. In doing so, the authors highlight the continuity, change, and innovations of cultural practices and behaviors that contributed to such remarkable diversity. An assessment of “transitional” post-Lapita sites indicates that the complex motifs on Lapita pottery reflecting social markers were retained in Near Oceania for several centuries longer than in Remote Oceania, where diverse social identities were quickly forged within and between island groups. Settlement patterns, social networks, and subsistence strategies were continually adapted to localized constraints and, together with ongoing population migrations and cross-cultural interaction, contributed to the Pacific region becoming one of the most culturally diverse regions in the world.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.027
GPT teacher head0.229
Teacher spread0.202 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2024
Admission routes1
Has abstractyes

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