Farming through change: using photovoice to explore climate change on small family farms
Bibliographic record
Abstract
This research utilizes photovoice to examine how farmers on small family farms in central North Carolina are experiencing vulnerability to climate change. Understanding the adaptive behaviors of farmers is critical in fostering the resilience of these individuals, communities, and local food systems. A case study of seven farmers across six farms in Chatham County was conducted. Farming tenure ranged from one year to over 40 years. Farm size ranged from less than one to 200 acres, and included certified and uncertified organic farms. Over a 5-month period, farmers were provided digital cameras to photograph issues or events on their farm related to a changing climate and focus group meetings were held to discuss the photographs and their significance. Findings indicate that developing effective social networks, implementing new adaptive behaviors such as polyculture, agrivoltism, seed saving programs, and flexible plantings may boost small farm resilience.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".