New and Young Farmer Participation in Agricultural Planning in the Township of Langley
Bibliographic record
Abstract
A farm succession crisis looms due to the ageing farm population, barriers facing emergent farmers, and a lack of succession plans to smoothly transition farms once their operators retire. Additionally, the agricultural sector exhibits a diversity of farm types, practices, markets, and crops, and is simultaneously facing increasing economic, social, and environmental issues. Agricultural planning is a means to address disputes involving agricultural development, farmland protection, and decision-making. Planning may also reconcile the competing interests for access and land base use. It is unclear what avenues exist for emerging farmers to contribute to this political process. To explain the practices employed in the Township of Langley that influence farmer participation levels, this study addressed the following: How are new and young farmers engaging in the agricultural planning process? What factors influence farmer participation? A case study methodology was employed, utilizing interviews with seven farms, a farm practices survey, and a document review. Analysis determined that farmers are contributing to agricultural planning through consultations and informal ways (board members on local nonprofits and farmers groups) that represent lower levels of involvement. Additionally, significant barriers prevented young and new farmer participation, including time constraints, lack of knowledge of how to become involved, and increased effort to find out about engagement processes. Furthermore, new and young farmers are contributing to their local food networks and employing practices aligned with food sovereignty movements. A characterization of policy problems in the Township indicated that more participation is required then currently employed. In order to increase participation, policy makers and planners in the Township must first increase communication flows in order to build trust with 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".