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Record W6978646807 · doi:10.7939/r3-9z9v-v874

Determinants of game meat consumption among hunters residing in Alberta

2024· dissertation· en· W6978646807 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2024
Typedissertation
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Consumption (sociology)PopulationAppealNatural resourceBushmeatTasteGame reserve

Abstract

fetched live from OpenAlex

The province of Alberta’s diverse ecology and large population of wild game poses the importance of management practices to keep the species population from overpopulating and encountering disease and food shortage. While hunting facilitates these management practices, the success of management strategies depends on a sustainable number of hunters and their continued participation in hunting. Government of Alberta (2022) reports indicate that fewer people have been hunting in recent years, which has caused concerns about declining hunting biological, economic, and social benefits. A potential strategy to encourage big game hunting in Alberta is to promote the harvest of own meat among hunters, which current research has consistently cited as the socially accepted reason for hunting. A survey was conducted to explore how food-related benefits of hunting strengthen the appeal of hunting wild game for food. Eighty-seven game hunters residing in Alberta participated in this on-line pilot study. Findings indicate that the committed game hunters in this study are actively involved in hunting and consuming game meat and intend to continue doing so. The primary source of game meat for these hunters is either from their own successful hunts or from their friends and families who engage in hunting. The primary motivating factors for hunting are the opportunity to spend time outdoors, relaxation, and obtaining natural food from local sources. Meanwhile, the key drivers for game meat consumption are the quality and freshness of the meat, a desire to utilize natural resources sustainably, and the taste of game meat. However, the limited access to land and hunting opportunities poses significant barriers to hunting and game meat consumption among these hunters. Despite this, there is substantial awareness and recognition of the benefits of game meat, and game hunters have a positive attitude towards meat and game meat consumption. They are highly involved with food, which leads them to try new foods and change their eating habits. Moreover, they are highly aware and concerned about environmental degradation consequences for themselves, others, and the natural world. Results from the descriptive analysis suggest that key factors that impact game meat consumption frequency are hunting frequency, motivation for hunting, game meat consumption motivation, level of food involvement, environmental beliefs, attitudes toward meat and intention to hunt and consume game meat. Correlation analysis highlights the importance of hunting frequency, hunting motivation, level of food involvement and intention to consume game meat. The study findings shed light on the efficacy of using food-related, free word association, and meat-eating and food-involvement questionnaires as practical tools to achieve the goal of documenting food-related benefits of hunting. While further investigations are required to obtain a more comprehensive understanding, this research makes a meaningful contribution to the limited research on this topic in Alberta. The practical insights gained from this study can guide future research and offer practical information to wildlife conservation stakeholders.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.118
Threshold uncertainty score0.237

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.194
Teacher spread0.186 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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