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Record W1553019946 · doi:10.3138/topia.18.127

“Do No Evil”: Google and Evil as a Political Category

2007· article· en· W1553019946 on OpenAlexvenueno aff
Imre Szemán

Bibliographic record

VenueTOPIA Canadian Journal of Cultural Studies · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
Fundersnot available
KeywordsSpectacleAsidePoliticsNoveltyConsumption (sociology)CommoditySociologyEnvironmental ethicsPolitical scienceAestheticsEconomicsLawSocial sciencePsychologySocial psychologyPhilosophyMarket economy

Abstract

fetched live from OpenAlex

The rapid proliferation of roads and vehicles in North America over the past several decades has made the problem of roadkill so acute that, setting aside the meat industry, automobile collisions now surpass hunting as the leading human cause of vertebrate mortality. Unfortunately, roadkill is still a largely overlooked problem that has not been seriously taken up by major animal-rights or environmental organizations. In the absence of any coherent moral or political discourse addressing the problem, commodity culture itself has effectively been delegated the task of reckoning with the carnage, generating a huge array of roadkill novelty goods that offer the animal’s desecrated body up for consumption as a comic spectacle of abasement and domination. Attempting to rectify this absence, this paper employs the notion of commodity fetishism to examine roadkill both as the flashpoint for collective anxieties surrounding the status of animals in consumer culture, and as a window onto broader structural problems arising from the spread of automobile-oriented transportation systems over the past century.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score0.909

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 studies0.0010.001
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 head0.055
GPT teacher head0.375
Teacher spread0.320 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations6
Published2007
Admission routes1
Has abstractyes

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