Shifting Stories, Changing Places: Being Caribou and Narratives of Transformational Climate Change in Northwestern North America
Bibliographic record
Abstract
In April of 2003, Leanne Allison and Karsten Heuer set out on skis, from north of the Gwich’in village of Old Crow in the Canadian Arctic, to follow the Porcupine caribou herd on its annual migration. The goal of their expedition was to tell the story of the caribou, so that policy makers and ordinary people would understand the importance of protecting their calving grounds, which President George W. Bush had vowed to open to oil and gas leasing. This dissertation traces the Being Caribou expedition story, as told through the Being Caribou film, book, children’s book, website, blog posts, slideshows, and speaking presentations, to analyse its impact in promoting calving grounds protection. \n\tThe research builds upon established forms of film and media analysis by situating the Being Caribou stories within a thousands of years long relationship between caribou and Gwich’in and Inuvialuit people. Taking this long view brings forward the vital role of northern indigenous communities in shaping all aspects of the Being Caribou journey, from the parks and protected areas Allison and Heuer passed through, to the tenor of the expedition’s media products and outreach. \n\tIn the 2004-6 period, the Being Caribou film was systematically used by the Alaska Coalition to build participation and leadership in a broad-based movement to influence crucial Congressional votes on the fate of the Arctic Refuge calving grounds. Through an analysis that combines film and participatory culture research frames with insights from civic engagement literature, this dissertation demonstrates how the storywork of Being Caribou house party and community screenings not only educated individuals about the calving grounds, but moved individuals up an “activist ladder” (Hahn, 2014) of social movement participation. Hundreds of thousands of North Americans who attended Arctic Action Day Being Caribou screenings wrote letters, signed petitions, attended demonstrations, met with their elected officials, and otherwise took leadership to oppose development within the Arctic Refuge. Over time, the Being Caribou film, books, blog posts, slideshows and speaking presentations helped to challenge the dominant values of North American petroculture, growing an ‘ecology of story’ in which the caribou, and their calving grounds, have flourished.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.026 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".