How permitted non-farm uses impact agriculture in the agricultural land reserve : an assessment in six Greater Vancouver municipalities
Bibliographic record
Abstract
An Agricultural Land Reserve (ALR) was established in British Columbia, Canada, by provincial legislation in 1973 with the objective of protecting agricultural lands and encouraging its use for agriculture. The regulation restricts the use of reserves lands to agriculture and related purposes, but approval for permitted non-farm uses can be granted by the reserve’s oversight body, the Agricultural Land Commission. This study assessed whether permitted non-farm use activities serve to enhance or detract from agricultural use of the land subject to the decision. A methodology to track and assess agricultural land use post non-farm use approval was developed and applied. This included a review of documentation related to approved non-farm use decisions in six contiguous municipalities in the greater Vancouver region of British Columbia, Canada, from 1997 to 2016. followed by contemporary land use assessment and data analysis. As such the study comprised three stages. Overall, approved non-farm use applications do not lead to more or less agricultural use of ALR lands. Most parcels not farmed prior to approval of non-farm use, remained not farmed and those used for farming continued to be farmed. As such, the analysis indicates that on balance there was neither an outright positive or negative outcome. Study results provide a snapshot of a period of time and are not intended to suggest a causal relationship. This study contributes to a greater understanding of the impacts of approved non-farm use decisions on land designated for agriculture.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".