Bibliographic record
Abstract
For local economic developers, succession planning is becoming a significant issue due to the aging workforce. This is especially true in rural communities, like Haldimand County, where the economic base is farming, as the average age of farmers continues to increase while fewer youth are entering the profession. Promoting workforce development through succession planning will increase the likelihood that capable and skilled farmers will continue to farm, which will improve economic stability and reduce the risk of farm business failure. This research is focused on assessing whether or not farmers in Haldimand County are aware of the succession planning process; determining if adequate resources are available to help farmers with the succession planning process; and through a gap analysis identifying key challenges farmers experience during the succession planning process that are not addressed through the available resources. This research will help economic development researchers and practitioners better promote and increase the use of farm succession planning in their region, which in turn will lead to stronger rural planning and development. At the regional level, increased succession planning will improve local food security and safety as knowledge of future farm land use will be more accessible. Succession planning will also improve rural communities land use policies as municipal governments will be able to better predict their future land use needs. Finally, workforce development planning initiatives will be enhanced in the agriculture sector as farm succession.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.015 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".