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Record W4404814189 · doi:10.3828/hgr.2024.34

Growing up North

2024· article· en· W4404814189 on OpenAlexaffabout
Robert W. Park

Bibliographic record

VenueHunter Gatherer Research · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

The archaeologists who study childhood are ourselves the end product of an approach to learning that is antithetical to how learning occurred in one of the most evocative of foraging cultures: the Inuit. Archaeologists-to-be in most societies undergo direct instruction – ie formal teaching – throughout the many years of their education, starting in preschool. At all levels, from preschool through doctoral studies, learners are encouraged to ask, and are rewarded for asking, questions about everything. Each learner encounters many teachers, who provide active instruction and formally and rigorously evaluate the learner’s progress in acquiring the information or skills being taught. In stark contrast to this, Inuit children were expected to learn on their own, through observation and experimentation. Both direct instruction by adults, and the asking of questions by children, were actively discouraged. In today’s jargon, Inuit foragers emphasise experiential learning. This paper will summarise some of the ethnohistorical information concerning Inuit learning and explore some of the archaeological correlates and implications of emphasising that mode of learning.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.264
Threshold uncertainty score0.883

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.2640.089

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 head0.235
GPT teacher head0.395
Teacher spread0.159 · 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 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

Citations0
Published2024
Admission routes2
Has abstractyes

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