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Record W4403878366 · doi:10.1017/eaa.2024.36

Playing to Survive: Children and Innovation During the Little Ice Age in Greenland

2024· article· en· W4403878366 on OpenAlexaboutno aff
Mathilde Vestergaard Meyer, Felix Riede

Bibliographic record

VenueEuropean Journal of Archaeology · 2024
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsIce ageHistoryPhysical geographyGeographyGeologyPaleontology

Abstract

fetched live from OpenAlex

Greenland is the world's largest island, but only a narrow strip of land between the Inland Ice and the sea is inhabitable. Yet, the Norse chose to settle here around ad 986. During the eleventh century ad, precontact Inuit people moved into Greenland from northern Alaska via Canada. Although the two cultures faced the same climatic changes during the Little Ice Age, the Inuit thrived, while the Norse did not, for multiple causes. The authors focus on one of these causes, the hitherto overlooked contribution of young children's learning strategies to societal adaptation. The detailed analysis of a large corpus of play objects reveals striking differences between the children's material culture in the two cultures: rich and diverse in the precontact Inuit material and more limited and normative in the Norse. Drawing on insights from developmental psychology, the authors discuss possible effects of play objects on children's future adaptability in variable climatic conditions.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.390

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0080.010
Scholarly communication0.0060.003
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.338
Teacher spread0.310 · 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 designNot applicable
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

Citations2
Published2024
Admission routes1
Has abstractyes

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