Waiting for the snow to fall: First Nations, federal policy and environmental justice
Bibliographic record
Abstract
Critical evaluation of the proposed expansion of the Arizona Snowbowl ski area will provide knowledge towards developing sustainable land-use policy.The San Francisco Peaks region in Arizona has endured numerous conflicts since the United States Forest Service (USFS) allowed the ski area's construction in 1938.The cultural importance of this region is evident in that over thirteen Native American tribes hold this area as sacred and central to their traditional way of life.Through this conflict, partnerships fostered between the tribes and several environmental groups have illuminated the significance of the cultural and natural resources involved.However, lost in the consciousness of many Americans today is the fact that Indigenous peoples have been fighting to save their sacred sites since Europeans made first contact.The ski industry developed without oversight for decades even with negative impacts to both environment and community.The ski industry has failed to address these impacts due to the strength of corporate power that drives the industry.For example, loopholes found in the National Environmental Policy Act (NEPA) will allow the Arizona Snowbowl to spray wastewater over an uncontaminated mountainside for making artificial snow, which simultaneously damages the spiritual significance of the mountain for Native Americans.I will use a methodological approach centered on qualitative interviews to examine historical actions that have paved the way for the present conflict involving the Arizona Snowbowl.Literature reviewed will examine Native American culture and iv sacred sites, United States-Tribal relations, the history of the ski industry, and ecosystem health as integral to the understanding of this case study analysis.The purpose is to contribute to achieving greater governmental accountability, cultural sensitivity, and recommendations for sustainable recreational activity.Through this thesis, I intend to articulate the arguments and their reasoning behind this conflict, analyze its current structure, and make recommendations for a socially and environmentally just outcome.Through in-depth interviews I will gather data to identify differing viewpoints so that this thesis may serve as a basis for understanding the greater issues connected to this case, including ski industry and USFS accountability, as well as sustainable and culturally equitable environmental policy.Environmental justice theory will be the conceptual framework of this analysis.To resolve this conflict, policy crafted to address the differing values and diverse constituencies is necessary.The implications intend to add to the dialogue for constructing sustainable land-use policy that centers on social and environmental justice.v ACKNOWLEDGEMENTS I would first like to thank family and friends for their love and support.This paper is a gift to my father's spirit,
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.019 | 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".