Protecting Agricultural Land: How Informal Institutions and Historical Perspectives Affect Land-Use Policy
Bibliographic record
Abstract
Land-use pressures in Alberta's agricultural landscapes have intensified in recent years. With the province's broad historical agricultural base and ongoing urban expansion, there have been growing concerns about the loss of prime agricultural land. These concerns and conflicts have been reflected in recent provincial policies that attempt to balance competing land-use pressures. These policies include the 2008 Land-Use Framework (LUF) and the 2009 Alberta Land Stewardship Act (ALSA), which authorizes the creation of policies and management strategies to protect, conserve and enhance agricultural land (ALSA: Government of Alberta, 2009). However, local interpretations concerning province-wide land-use policies and perceived restrictions on private land use have hindered the desired outcomes (Lavelle, 2012). Since the creation of Alberta’s most recent land use policies, various studies have reported a persistent pattern of fragmentation and conversion of prime agricultural lands. While several research projects have measured the spatial context and implications of converting agricultural lands, few have attempted to assess the non-spatial causes of continuing conversion and fragmentation of agricultural land. Following a qualitative case study approach, this research aims to understand better how factors such as social norms and informal institutions influence land-use decisions at the municipal level, focusing on decisions affecting the fragmentation and conversion of agricultural land. This study also considers how these informal factors affect the application of Alberta's land-use policies and formal mandates.
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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 | 0.003 | 0.005 |
| 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 | 0.009 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".