Ecologies of Empire: Annie Proulx’s Climate Colonial Realism
Bibliographic record
Abstract
At the end of Annie Proulx’s Barkskins (2016), Sapatisia Outger, a Métis forest ecologist, witnesses glaciers disintegrate off the coast of Kalaallit Nunaat (Greenland) with her fellow environmental activists. Seized by the unsettling episode in this moment, she apprehends the historical confluence of settler colonialism and climate change: she suffered a full-force shock of recognition—the coming disappearance of a world believed immutable. She had heard for years that the earth and its life-forms were sensitive to slight temperature changes, that species prospered and disappeared as weather and climate varied, but dismissed these alarms as environmental determinism. On the ice her thinking shifted as the moon shifts its position in the sky … . “My God, how violently it is melting,” she had whispered to herself. Great fissures thousands of feet deep opened by meltwater that eroded the hard blue ice, fissures that gaped open to receive the cataract’s plunge, down to the rock beneath the great frozen bed, forcing its under-ice way to the sea, lubricating the huge cap from below. Standing near the brink of one ghastly thundering abyss someone said, “We are looking at something never before seen.” That night … everyone admitted being shaken by the living evidence. (712)
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".