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 is a gift to my father's spirit, who left this life before he was able to read the final draft.I would like to thank Humboldt State University, the Environment and Community M.A. program, professors, and my cohort members for unlimited support and dialogue over the past few years.I would also like to give thanks to Marlon Sherman and Mark Baker for their support as my committee members.Special thanks to my chair, Maria Bartlett, for her care and support during this process.I would also like to thank Deb Waxman for her time and consideration in working with me.As well, a sincere thanks to Kelvin, Evon, and the Save the Peaks folks for inspiring people to engage in this research and continuing to fight for what they believe in.I would like to thank all interviewees fortheir time and perspective shared.In addition, a sincere thanks to Jeanne Souldern, who finally helped this paper find its completion.I would like to acknowledge my thanks and respect for the Earth, for all of its life forms and processes, especially the snow and mountains.Finally, to Jillian, Olas, and Nieve for their unlimited patience and the loving support to make this into something tangible and accessible beyond academia.
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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".