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Record W1559279069 · doi:10.7202/017571ar

More bears, less bears: Inuit and scientific perceptions of polar bear populations on the west coast of Hudson Bay

2008· article· en· W1559279069 on OpenAlexvenueaboutno aff
Martina Tyrrell

Bibliographic record

VenueÉtudes/Inuit/Studies · 2008
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsArcticBayUrsus maritimusFeelingGeographyThe arcticPerceptionPolitical scienceEcologyPsychologySocial psychologyArchaeologyOceanography

Abstract

fetched live from OpenAlex

Inuit and scientific perceptions of polar bear populations are grounded in different epistemologies, relationships and interactions with polar bears. In many communities, the presence of polar bear hunting quotas has led to both external and internal conflicts. Inuit throughout Nunavut are seeing more polar bears in close proximity to their communities. Scientists argue that the increase in bear-human encounters is due to rapid environmental change, leading to a decrease, rather than an increase, in polar bear numbers. These opposing perceptions result in confrontation regarding the hunting and protection of bears. Within communities, ethical, social and economic conflicts arise with regard to the enactment of the quota system due to differing views on the allocation of the quota, the existence of a sport hunt, and the morality of such an intense focus on polar bears. One such community is Arviat, on the west coast of Hudson Bay. The people of Arviat feel particularly vulnerable to the year-round presence of polar bears in and near their community. The inclusion of women in the hunt and the cost of undertaking a bear hunt, lead to discussion and mixed feelings about the open hunting season each year. While the situation in Arviat is in some ways unique, it also serves as an example of the questions and concerns facing Inuit across the Canadian Arctic as they increasingly have to deal with polar bears on their doorstep.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0070.002
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.137
GPT teacher head0.398
Teacher spread0.261 · 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 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

Citations47
Published2008
Admission routes2
Has abstractyes

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