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Record W1504422591 · doi:10.7202/016147ar

Everyone goes fishing: Understanding procurement for men, women and children in an arctic community

2007· article· en· W1504422591 on OpenAlexvenueaboutno aff
Kerrie Ann Shannon

Bibliographic record

VenueÉtudes/Inuit/Studies · 2007
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsEthnographyFishingArcticThe arcticProcurementComplementarity (molecular biology)Division of labourGender studiesGeographyEconomic growthPolitical scienceSociologySocioeconomicsBusinessAnthropologyEcologyEconomicsOceanographyMarketing

Abstract

fetched live from OpenAlex

This paper provides insight into Inuit procurement and gender roles. Through a focus on fishing derbies in the Canadian Arctic, this significant aspect of Inuit life is recognized. Many ethnographies and land use studies have previously concentrated on hunting. The fishing derby provides an alternative ethnographic example of procurement. It is an activity in which women, men, children, and elders participate. Women’s roles in the Arctic have often been discussed in terms of gender division of labour or in terms of their complementarity to men’s roles. The fishing derby demonstrates occasions when procurement activities are not necessarily divided along gender lines and thereby reveals a broader understanding of gender roles. The fishing derby is also an ethnographic example of skill as traditional knowledge and may inform how Inuit, and hunter-gatherers more generally, relate to the world around them.

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)0.004
metaresearch head score (Gemma)0.003
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 score0.558
Threshold uncertainty score0.889

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0200.012
Scholarly communication0.0060.005
Open science0.0020.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.218
GPT teacher head0.440
Teacher spread0.222 · 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

Citations12
Published2007
Admission routes2
Has abstractyes

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