Policy brief: Towards a Future-Proof Public Service Media? Lessons from a comparative analysis in seven media markets
Bibliographic record
Abstract
Public service media (PSM) all over the world have consistently been subject to different forms of societal and technological transition. However, recent years have brought a number of new challenges. These include radical changes in media use, the advent of streaming services and the dominance of big tech. Moreover, the increasing diversity and polarisation of societies have led to the erosion of trust in traditional media. These have challenged the legitimacy of public service media as an institution and project, but have also affected the broadcasters themselves, requiring them to transform into fully digital, online-first organisations. In this context, we present results from the research conducted during the first year of PSMAP, a large-scale comparative research project analysing ‘Public Service Media in the Age of Platforms’. In this brief, we provide an overview of the dimensions of platformisation, and a series of core findings and discussions on PSM and platformisation, based on the analysis of media laws, broadcast contracts and licences, annual reports, and current policy debates in the following markets: Belgium (Flanders and Wallonia), Canada, Denmark, Italy, Poland, and the UK.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".