Bibliographic record
Abstract
Abstract Critical political economy has emphasized the tensions and power relations between global forces and local forces as well as the political and the economic. Since media ownership has become one of the major elements to widening the existing gaps between a few powerful actors and the majority of underprivileged players, critical political economy focuses on the significant role of ownership in media and communication studies. Critical political economy has also continued to emphasize the structural change in media industries in the broader socio-economic milieu. In the early 21st century, critical political economy has shifted its emphasis toward digital platforms, such as over-the-top service platforms like Netflix, social media platforms like YouTube, and search engines like Google, as these digital platforms supported by artificial intelligence algorithms and big data are primary actors in the global cultural industries. They are not only shifting the milieu surrounding cultural industries but also transforming the entire value chain in cultural production, including the production of popular culture, the circulation of cultural products, and the consumption of cultural content. Critical political economy needs to analyze power relations between platform owners and platform users as well as between a few countries in the Global North that possess these digital platforms and the majority of countries in the Global South that, owing to the lack of capital, manpower, and know-how, cannot advance their own platforms. This implies that critical political economy needs to analyze how global digital platforms as part of Western cultural industries have controlled and manipulated local cultural industries. By discussing the change and continuity in the cultural industries in the digital media–driven media environment, which expedites the concentration of the industry, new international division of labor, and platform imperialism practice, critical political economy will shed light on the existing debates about power relations within the broader 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".