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Record W4412177168 · doi:10.2979/gbs.00020

Zombies in the Snow: Contemporary Visions of the Undead North

2025· article· en· W4412177168 on OpenAlexaboutno aff
Dragoslav Momcilovic

Bibliographic record

VenueThe Global South · 2025
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsVisionSociologySnowGeographyAnthropologyMeteorology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.048
GPT teacher head0.379
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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