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Culture Jamming

2017· other· en· W4238593015 on OpenAlexaff
Jay M. Handelman, Robert V. Kozinets, Alexander I. Mitchell

Bibliographic record

VenueThe Blackwell Encyclopedia of Sociology · 2017
Typeother
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsYork UniversityQueen's University
Fundersnot available
KeywordsMaterialismHegemonyJammingSociologyIdeal (ethics)Political scienceConsumption (sociology)Power (physics)Public relationsCritical discourse analysisMedia studiesCultural hegemonyMass mediaLawEpistemologyIdeologySocial sciencePolitics

Abstract

fetched live from OpenAlex

Abstract Culture jamming refers to an organized, social activist effort that uses inverted, similar broadcast techniques to counter mass media messages that support the current technocapitalist system. Culture jammers often target issues such as the social equality, materialism, and environmental problems of technocapitalism, and they are often embroiled in debates around freedom of speech and expression. The concern may be related to Jürgen Habermas, for whom an ideal speech situation is one in which all participants within a public space are empowered to reach consensus on issues of mutual importance through engagement in symmetrical discourse. What motivates activists engaged in culture jamming, however, is a view that contemporary communications are distorted by the power of media companies. Here activists regard symmetrical public discourse as being eroded by corporately controlled mass media that have come to serve as a culturally omnipresent venue through which corporate‐sponsored advertising shapes a logic of consumption. Culture jamming, then, is the attempt by activists to break through this corporate‐controlled, distorted, asymmetrical public discourse that has led to a hegemonic cultural logic whereby consumption comes to permeate all aspects of lived experience.

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, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.142
Threshold uncertainty score1.000

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 studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.323
Teacher spread0.299 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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