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Record W3207732250 · doi:10.14430/arctic73281

Hunting by Early Modern Lule Sami Households

2021· article· en· W3207732250 on OpenAlexvenueno aff
Jesper Larsson, Eva-Lotta Päiviö Sjaunja

Bibliographic record

VenueARCTIC · 2021
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
FundersVetenskapsrådetKungl. Vitterhets Historie och Antikvitets Akademien
KeywordsSubsistence agricultureTrophyGeographySubsistence economyProperty rightsResource (disambiguation)FishingCorporate governanceEconomyAgriculturePolitical scienceBusinessEconomicsArchaeology

Abstract

fetched live from OpenAlex

Hunting was one of three pillars, along with fishing and reindeer husbandry in the early modern Sami economy, and understanding of Sami hunting has increased during recent decades. However, most research has concentrated on time periods before AD 1600. After AD 1600 and the initial formation of modern Nordic countries, hunting ceased to be the backbone of the overall Sami economy but continued as an integral part of household economies. Our aim is to advance understanding of early modern hunting in northwestern interior Fennoscandia. Using source materials including court rulings and historical accounts, we set out from a self-governance perspective focusing on how actors solved resource distribution with regards to hunting. We show that ecological differences between mountains and forest impacted decisions about hunting. From the 1500s to the end of the 1700s, hunting led to the extinction of wild reindeer and depopulation of fur animals, while small-game hunting for subsistence continued to be important. In the forest region, strong property rights to game developed when skatteland (tax land) was established and hunting became a private enterprise. We suggest that the institution of skatteland was a response to changes in Sami economy, and the transition from collective to individual hunting was a contributing factor.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
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 studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.041
GPT teacher head0.342
Teacher spread0.301 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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Same venueARCTICSame topicIndigenous Studies and EcologyFrench-language works237,207