Determinants of game meat consumption among hunters residing in Alberta
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».