Bibliographic record
Abstract
ABSTRACT: The Far North stokes the cultural imagination, particularly as new generations of writers and filmmakers reinventing the contemporary zombie apocalypse narrative look to the Arctic as a blank slate upon which to project timely concerns about environmental degradation and the exploitation of people and resources; the widening global wealth gap; and the continued resurgence of public health crises, xenophobia and authoritarian cultures of rule. In this article, I trace the different ways this much-mythologized geopolitical landscape reflects and elaborates some of these global and planetary risks in a recent spate of speculative narratives that shift the zombie outbreak into the inhospitable landscapes of the Far North. First, I argue that contemporary zombie narratives set in the Far North offer uncanny recreations, with haunting distortions, of the material histories and relations of the Global South that produced the first iteration of the modern zombie, the Caribbean zombi . Second, I argue that the images of the Far North that appear in such narratives curate a vision of what I want to call the Undead North. Finally, I offer close readings of one such zombie text of the northern variety, the Netflix streaming series Black Summer (2019–2021), which projects an image of the Undead North as an imaginary geography of the Canadian Far North, one that allegorizes larger historical and environmental processes that have reached crisis points in the twenty-first century—including the unchecked and uneven growth of the military-industrial complex and the failed project of settler colonialism.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.021 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".