Avenues of Agricultural Literacy: A Grounded Theory Model to Increase Agricultural Literacy Effectiveness
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".