“Political economic research continues to explore the concentration of media ownership and the consequences of commercialized media for a consumer society”: Interview with Janet Wasko
Bibliographic record
Abstract
Eptic : How do you observe the context of the research focus of the Political Economy of Communication within the U.S. academic community in the XXI century? In what direction are the main researches in the U.S. currently heading? Janet Wasko : The study of the political economy of communications in the US continues to provide an important and essential analysis for media studies. While mostly ignored by mainstream media economists and rejected by many cultural theorists, the tradition is continuing to grow, especially among new communication scholars. Political economic esearch continues to explore the concentration of media ownership and the consequences of commercialized media for a consumer society. US and Canadian scholars also are developing an even more sophisticated theoretical foundation, as evidenced by a number of new books relating to theories of political economy and media. An interesting development is the tendency for younger scholars to integrate political economic analysis with cultural theories, thus providing an even more compelling explanation of role in contemporary society.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".