Maine Monsters: How Indigenous and Non-Indigenous People Perceive Environmental Monstrosity
Bibliographic record
Abstract
Wilderness is a creation of the human mind. Wilderness reflects our desires, fears, and truest selves—therefore within it we often find monsters. The application of monstrosity to the natural world is an act of projection and an accumulation of the cultural and historical influences that shape the perceiver. It’s often a reflection of religion—e.g. European gods associated with agriculture, while their monsters and demons roam the woods—and varies across peoples. This thesis seeks to understand how people create and assign monstrosity from their own mind to the environment around them, and in turn how they perceive it. Specifically, it explores the question of how these perceptions differ between Indigenous (Wabanaki) and non-Indigenous peoples in Maine. While the primarily European settlers of what we know as the United States of America may hail from cultures that subjugate the environment, this is not true of those who know the land as Turtle Island. How this may influence perceptions of monstrosity has yet to be learned. This thesis will attempt to learn through an analysis of environment- and monster-related Maine storytelling, as projection also means representation. The study is largely based on discourse and value analysis and uses three core fundamentals— wilderness, monstrosity, and storytelling—to paint a picture of environmental monstrosity. It also uses the partial juxtaposition of two Maine ecosystems—mountains and the ocean—to highlight the differences between Indigenous and non-Indigenous perceptions of wilderness and monstrosity. It wraps up with a look into how the results of the analysis may influence environmental management, stewardship, and other issues related to environmental monstrosity and storytelling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".