Sharing the range: the challenges and opportunities for sustainable ranching and habitat conservation in the municipal district of Pincher Creek
Bibliographic record
Abstract
The broad scope and intent of this thesis is to contribute to the body of research and writing about the loss of agricultural land due to development and the transformation of rural agricultural communities. At the more specific level, through interviews and secondary research, this thesis considers municipal land use planning in Alberta under the revised 1995 Municipal Government Act in the Municipal District (MD) of Pincher Creek No. 9, where cattle ranching, wildlife, and now, acreages vie for land resources. The critical questions addressed are: What are the conflicts between ranching and habitat conservation, and conversely, what opportunities do they share? What role can and does a municipality play in promoting sustainable ranching and conservation through its land use policy and jurisdiction? Set in southwest corner of Alberta, the MD of Pincher Creek is endowed with a remarkable history of ranching, ample resource wealth, and a unique climate and topography that supports a spectacular, rich, diverse ecosystem. Within the past few years, private agricultural land near Waterton Lakes National Park and the Castle River wilderness in the MD has come under speculative and development pressure predominantly for country residences, often retirement homes, and for tourism interests. Recent Municipal Act amendments have delegated substantially more land use control to rural municipalities, as a result the MD of Pincher Creek has more authority to make decisions that shape its future community profile, to mediate between competing land use interests, and to impact local ranching and habitat. The thesis analysis explores how the best practices of ranching or "sustainable ranching" can help to conserve and enhance habitat and how ranchers' attitudes can evolve to be more tolerant of wildlife. This thesis also explores and supports the efforts of a budding local land trust, SALTS, which plans to protect local agricultural land and habitat through conservation easements. Finally, the thesis concludes by envisioning ways the MD government can encourage habitat preservation, conservation easements, sustainable and economically viable ranching, as well as the control and direction of country residential development, all with a view to ensuring that future economic development opportunities remain available for local residents.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".