GENERATIVE LEADERSHIP DEVELOPMENT IN AN AGRICULTURAL LEADERSHIP PROGRAM
Bibliographic record
Abstract
Adult agricultural leadership programs (ALP) train people to address the needs of a diversifying society with pressing social, economic, environmental, and political challenges. Additionally, these programs offer transformative learning experiences that lead to a greater capacity of current and prospective leaders to become change agents in their communities. In a profession where vitality, strength, and perseverance are fundamental, the agricultural industry needs leaders who remain aware of the foundational knowledge contributed by their predecessors. At the same time, it also necessitates innovation that may revolutionize the agricultural industry for decades to come. In this mixed-method study, we asked participants of a state-based ALP to complete the Loyola Generativity Scale (N=48) that measures generative concern, with higher scores indicating stronger generative concern. Survey results (N=48) indicated average overall generative concern. However, there was a considerable variation among participants, scores ranging from 45 to 77. To understand the range of attitudes, we conducted interviews (N=11) with ALP participants. Generativity Theory provided the foundation of our qualitative analysis. We identified how participants are acting generatively in their leadership roles by promoting the sustainability of agriculture through social engagement, capitalizing on opportunities for teaching and learning, and expanding social capital through intergenerational professional networks. From this research, scholars and practitioners will gain a more nuanced understanding of how this ALP is facilitating generative leadership among today’s leaders so they may continue transforming their industry by connecting generational cohorts through the transmission of experience, knowledge, and expertise.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".