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Record W2623826290 · doi:10.1177/1527476417712459

Literal Media Ecology: Crisis in the Conditions of Production

2017· article· en· W2623826290 on OpenAlexaff
Brett Caraway

Bibliographic record

VenueTelevision & New Media · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicCybernetics and Technology in Society
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEcological crisisProductivityProduction (economics)EcologyCapital (architecture)Ecological economicsEconomicsCapitalismNeoclassical economicsWork (physics)Environmental crisisSociologyPoliticsEnvironmental ethicsSustainabilityPolitical scienceLawMicroeconomicsEconomic growth

Abstract

fetched live from OpenAlex

This article outlines a socio-political theory appropriate for the study of the ecological repercussions of contemporary media technologies. More specifically, this approach provides a means of assessing the material impacts of media technologies and the representations of capitalist ecological crises. This approach builds on the work of ecological economists, ecosocialist scholars, and Marx’s writings on the conditions of production to argue that capitalism necessarily results in ecological destabilization. Taking Apple’s 2016 Environmental Responsibility Report as a case study, the article uses the theory to analyze Apple’s responses to ecological crises. The article asserts that Apple’s reactions are emblematic of the capitalist compulsion for increasing rates of productivity. However, unless the matter/energy savings achieved through higher rates of productivity surpass the overall increase in the flow of matter/energy in production, ecological crises will continue. Ultimately, capital accumulation ensures continued ecological destabilization.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.018
Scholarly communication0.0120.014
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.047
GPT teacher head0.281
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations13
Published2017
Admission routes1
Has abstractyes

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