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Record W1530342044 · doi:10.1080/10871209.2015.1046533

Hunting for Trophies: Online Hunting Photographs Reveal Achievement Satisfaction with Large and Dangerous Prey

2015· article· en· W1530342044 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueHuman Dimensions of Wildlife · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsTula FoundationRaincoast Conservation FoundationUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaHakai InstituteUniversity of Victoria
KeywordsTrophyPredationPleasureOddsPopularityPsychologyGeographySocial psychologyEcologyLogistic regressionComputer scienceBiologyArchaeology

Abstract

fetched live from OpenAlex

Despite its manifold implications, insight into what satisfactions hunters derive from trophy hunting has not been thoroughly investigated. We used a novel method to assess how common satisfaction might be from harvesting animals under different achievement contexts. We scored smile types—signals of emotion and satisfaction—in 2,791 online hunting photographs. We show that the odds of true “pleasure” smiles are greater when hunters pose: (a) with versus without prey, (b) with large versus small prey and, (c) with carnivores versus herbivores (among older men). We emerge with a generalizable achievement-oriented hypothesis to propose that the prospect of displaying large and/or dangerous prey at least in part underlies the behavior of many contemporary hunters. Given that achievement was also likely important among ancestral hunter-gatherers and remains so in contemporary cultural and commercial marketing contexts, management might benefit by increased attention to achievement satisfaction among hunters.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.481

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.257
Teacher spread0.231 · 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