Avenues of Agricultural Literacy: A Grounded Theory Model to Increase Agricultural Literacy Effectiveness
Notice bibliographique
Résumé
Agricultural literacy was a notion developed over 30 years ago to increase awareness of agriculture and cultivate an interest in agri-careers, ultimately to garner citizen support of the industry and secure a future workforce. However, awareness of agriculture and its importance is at an all-time low and vacancies in the agriculture industry are alarmingly high. While there are a number of reasons for this scenario, such as a departure from agrarian societies and a number of individual decisions that affect agri-career-uptake, opportunity exists for agricultural literacy efforts to have a more profound impact on these areas. The purpose of this study was to use a grounded theory approach to explore and identify themes, trends, gaps, and barriers that are helping or hindering the impact of agricultural literacy efforts and to offer theory that can inform and improve agricultural literacy program effectiveness. Initial data were collected from peer-reviewed published empirical and conceptual literature on agricultural literacy using the keyword searches “agricultural literacy,” “agricultural curriculum,” and “agricultural knowledge” in the Education Resources Information Center (ERIC) database. Additional literature was collected through concurrent data collection and theoretical sampling to situate findings in a Canadian context by filling in gaps and address arising questions. Initial, intermediate, and advanced coding were applied to 40 articles retrieved through initial data collection and 9 articles collected through theoretical sampling. Four thematic areas were identified as having potential to make agricultural literacy more effective. These four areas include definitions, goals, teaching and research approaches, and outreach confines. The avenues of agricultural literacy model is the resulting pragmatic product of this study, which serves to address the barriers identified in these areas. It offers revised definitions, identifies and links the goals to learning outcomes, curriculum, and research, and suggests agricultural literacy pair with a multiliteracies pedagogy. The final theme is addressed by suggesting advocacy for mandatory agricultural literacy courses in secondary schools with knowledgeable and experienced agriculture teachers. The avenues of agricultural literacy model can be used as a framework to guide current and future agricultural literacy programs.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».