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Record W357900577 · doi:10.3138/topia.32.135

Streaming Precarity: The Polar Bear Cam and the Display of Migration

2015· article· en· W357900577 on OpenAlexvenueaboutno aff
Constance Lafontaine

Bibliographic record

VenueTOPIA Canadian Journal of Cultural Studies · 2015
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsCommodificationPrecarityAnthropocentrismEnvironmental ethicsSociologyHistoryAestheticsGender studiesArtPhilosophy

Abstract

fetched live from OpenAlex

The figure of the polar bear is used more extensively than any other faunal or floral species as the image through which to express and communicate human anxiety about the effects of global warming on the North. This paper considers representations of polar bears in Canada by discussing the spectacularization of the animal afforded by the Polar Bear Cam, a live online stream of the species’ yearly migration near Churchill, Manitoba. The Polar Bear Cam, like several other popular contemporary representations of the polar bear, has been conjugated within a discourse of global warming. Precarity, threats of extinction and impending death operate as impetuses for the display and viewing of the animal. This article considers some points that arise at this intersection of sight and survival and argues that the Polar Bear Cam operates within an anthropocentric framework. In several ways, the Polar Bear Cam symbolically cements the animal as a being that is removed from the human, both geographically and ontologically. Even beyond its own endeavour of visual commodification, the cam is complicit with a system that is predicated on a sustained and institutionalized subjugation of the non-human. By discussing the polar-bear tourism industry in Churchill, I explore how spectacular displays of wildlife efface the complex ways in which humans and polar bears are connected.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.751
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.072
GPT teacher head0.366
Teacher spread0.294 · 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 designQualitative
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

Citations4
Published2015
Admission routes2
Has abstractyes

Explore more

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