Hobby Farm's and British Columbia's Agricultural Land Reserve
Bibliographic record
Abstract
Agricultural land protection near the urban-rural fringe is a goal of many jurisdictions, including British Columbia, Canada, which uses a provincial-wide zoning scheme to prevent subdivisions and non-agricultural uses of the land. Preferential taxes are also used to encourage agricultural use of the land. Small scale hobby farmers are present at the urban fringe near Victoria (the capital), both inside and outside of the Agricultural Land Reserve (ALR). The goal of this paper is to investigate whether hobby farms create problems for agricultural land preservation. We make use of a GIS (geographic information system) model to construct detailed spatial variables and analyse our parcel-level data set using an hedonic pricing model and a limited dependent variable model. The results show that hobby farmers tend to select small parcels that are near open space and relatively close to the city and they tend to support horses and other livestock. In terms of price, farmland is worth more per ha the smaller the parcel is and the closer it is to the city. In general farmland is worth more when it is less fragmented but this appears to be reversed for hobby farms – indicating that hobby farmers may be better adapted to surviving in the urban fringe than conventional farmers. The conclusions drawn from the results in this paper would likely apply to other jurisdictions which seek to protect agricultural land in the urban fringe.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".