An alternative form of strategic development planning for creating sustainable agricultures and conserving good quality farmland in the province of Québec, Canada
Bibliographic record
Abstract
The aim of this short article is to introduce an alternative approach to conserving good quality farmland while also creating sustainable agricultures. Many jurisdictions have introduced measures to conserve good quality farmland and the agricultural activities it supports through conventional land use planning. However, conventional land use planning can really only support good quality farmland and its farm activities as long as the political actors in charge of the management of such land use planning activities are committed to conserving good quality farmland and the farm activities it supports.1 Frequently however, in multiple jurisdictions the land use planners and their local (municipal) and regional (e.g. county) municipalities have removed land from agricultural land use zones to be used for subdivision and /or industrial park development. On the other hand, some countries or provinces have introduced legislation to protect good quality farmland, e.g. the Provinces of Québec and British Columbia in Canada, thereby drawing upon a higher level of government. However, while Québec for a long time had a role as a “top down’ government, it began to change this position once it recognized the values and abilities of actors on the ground particularly in relation to agricultural development and conservation issues.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".