Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".