GENERATIVE LEADERSHIP DEVELOPMENT IN AN AGRICULTURAL LEADERSHIP PROGRAM
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it