Understanding the Role and Contributions of the Incubator Farm Program in Creating the Next Generation of Farmers in the United States and Canada
Bibliographic record
Abstract
The farming sectors in the United States and Canada has seen a steady decline in the number of farmers since the end of World War II. However, this issue did not gain widespread attention until the early 2000s when the farmer population hit a low of 2% of the total population in both countries. Recognition of this phenomenon led to initiatives to address farmer succession and renewal. Incubator farm programs – defined as land-based farmer training and support programs – are one such type of initiative that specifically focuses on developing entry pathways for people from outside the farm sector. The goal of this study is to critically assess incubator farm programs (IFPs) as a new farmer support strategy that helps overcome structural barriers and enhances the agency of new farmers. This dissertation examines the incubator farm as a place-based entity influenced by the surrounding environment and explores its contribution to the farm succession landscape. In doing this, I use a number of approaches and methods. First, a quantitative survey (n=63) provides a high-level view of the programmatic space, including the population it serves and seeks to reach, the geographical settings of primary activity where opportunity and demand have met, and how the services provided empower beginning farmers. I then used a Sustainable Livelihoods Approach to tease out the assets that beginning farmers need to possess to be successful and analyzed (n=5) case studies to understand how IFPs helped build these assets and further empower beginning farmers. Finally, a single in-depth case study utilizes participant observation methods to unpack the relationship of an IFP to its locale. In undertaking this dissertation, I was able to better understand the inner nature of IFPs, a space that I have been connected with over the last 12 years. I unpacked the support and services provided and how IFPs contributed to building the capabilities of new farmers. By analyzing an IFP as a pragmatic place-based initiative I was able to delve deeper into the dynamics of IFPs and their locale. This dissertation also contributes to a better grasp of the IFP as a non-traditional entry pathway into farming, and its contributions to creating a new generation of farmers.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".