The Cost of Agricultural Land Preservation and the Sitting of Urban Development
Bibliographic record
Abstract
In North America a disproportionately high percentage of prime agricultural land is converted for urban development. Much controversy exists about this irreversible loss. Some claim that it will result in future food shortages, while others argue that technological advance has made agricultural land less scarce. There is no evidence of a looming food scarcity in the western world. Nevertheless, the future scarcity issue is shrouded in great uncertainty. Preserving prime land is a policy option to avoid possible future food contingencies. Preservation is not a costless undertaking, however. Costs are always defined in relation to the objectives pursued. In social economics the objective is usually defined as maximizing the net contribution to national product. This is not a meaningful objective if long time horizons, extreme uncertainty, and irreversibility are involved. In that case it is better to minimize possible maximum losses. This is akin to an insurance policy. The problem then is to choose premium payments and benefits in such a way that maximum possible future losses are minimized. The premium that society pays for preserving prime land is identified as the possible additional development, servicing, commuting, and environmental costs on lower compared to prime quality land minus the gain in productivity on prime compared to lower grade land. The premium is then compared to the benefits of the policy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".