Publishing Rights As A Solution To The Economic Crisis Of Mainstream Television Media? A Critical Analysis Of The Indonesian Press Council’S Proposal In Facing The Hegemony Of Digital Platforms
Bibliographic record
Abstract
The dominance of global digital platforms such as Google, Meta, and TikTok has triggered an economic crisis within Indonesia’s mainstream television industry, as evidenced by a significant decline in advertising revenue and increased audience fragmentation. This study critically examines the Indonesian Press Council’s proposal on Publishing Rights as a response to the economic imbalance between traditional media and digital platforms. By employing theoretical frameworks from Political Economy of Communication, Platform Capitalism, and Media Regulation, the research evaluates the mechanisms and implementation potential of Publishing Rights in the Indonesian context and draws comparisons with international practices in Australia, France, Canada, and South Korea. A qualitative approach is applied through policy analysis and comparative studies, supported by data obtained from policy documents, industry reports, and in-depth interviews. The findings indicate that Publishing Rights hold promise as an effective policy instrument to rebalance the digital media ecosystem, strengthen the resilience of quality journalism, and enhance the bargaining power of national media against global tech giants. Nevertheless, substantial implementation challenges remain, particularly resistance from digital conglomerates and the need for regulatory harmonization. The study recommends strengthening the legal framework through dedicated legislation, establishing a Digital Media Resilience Authority, and adopting a revenue-sharing model based on content production contribution. In conclusion, Publishing Rights have the potential to serve as a strategic pillar for the economic recovery of the television media industry, provided they are supported by an inclusive policy design and rigorous oversight.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".