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Record W1999418574 · doi:10.1163/15685306-12341262

What Good Is a Bear to Society?

2014· article· en· W1999418574 on OpenAlexaffabout
Lauren Harding

Bibliographic record

VenueSociety and Animals · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsWildernessCharismaMythologyNational parkNatural (archaeology)Environmental ethicsWilderness areaPsychologyGeographyHistorySociologyEcologyPolitical scienceArchaeologyLaw

Abstract

fetched live from OpenAlex

Abstract Arising out of fieldwork in the Canadian Rockies, this paper analyzes the role of bears in the conservation culture of Canadian national parks. Why is the presence of this large predator tolerated and even celebrated by some? And why do others fear and even despise this animal, whom they see as a danger and a menace, and resent its continued preservation? Bears may act as a token charismatic species in conservation mythology; they may be anthropomorphized into a cuddly roadside attraction evoking childhood nostalgia; or they may play the part of wrathful Nature guarding against human incursion into the wilderness. Tourists in Banff National Park take great pains to see bears, while local hikers and campers expend almost equal energy avoiding an ursine encounter. This paper explores what human reactions to bears reveal about social attitudes toward the natural world, particularly in areas like the Canadian Rockies where human and bear territory overlap.

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)0.003
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 score0.541
Threshold uncertainty score0.924

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.021
Scholarly communication0.0070.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.025
GPT teacher head0.320
Teacher spread0.296 · 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

Citations10
Published2014
Admission routes2
Has abstractyes

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