Eco-heroes out of place and relations: decolonizing the narratives of<i>Into the Wild</i>and<i>Grizzly Man</i>through Land education
Bibliographic record
Abstract
Eco-heroic quests for environmental communion continue to be represented, mediated, and glorified through film and media narratives. This paper examines two eco-heroic quests in the Alaskan ‘wilderness’ that have been portrayed in two Hollywood motion pictures: the movies Grizzly Man and Into the Wild. Both films vividly document and re-inscribe heroic status to the stories of Timothy Treadwell (Grizzly Man) and Christopher McCandless (Into the Wild), their tragic encounters with nature, and the pivotal experiences that gave them both eco-heroic identities in the American imagination. As is often the case for Greek and Shakespearean dramas, each hero met a tragic, unnecessary death in Alaskan ‘wilderness’, but in the process reiterated a settler colonial narrative. We argue that an Indigenous-focused Land education and its counter-narratives of holistic relations are sorely needed. It is Indigenous Land education that can break the cycle of Eurocentric celebrations of solitary heroism, rugged individualism, and ignorance of place. In order to forge Indigenous/non-Indigenous relations in our cultural imaginations and to address compounding environmental struggles, we need to turn to Indigenous stories and teachings that are already in place, in deep relation with the Land, water, animals and plants on Indigenous territory. We need to turn to Land education that is currently not in place or acknowledged in environmental education.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".