Streaming Public Service Television in the Age of Platforms: Lessons from a comparative analysis of VoD publishing and personalisation in the Belgian market
Bibliographic record
Abstract
Public service media (PSM) all over the world have consistently been subject to social and technological changes. However, recent years have brought several new challenges. These include radical changes in media use, the advent of streaming services, and the dominance of big tech. The new competitors for the attention of citizens have challenged PSM both as an institution and as organisations. The profound changes in the media landscape have affected the broadcasters themselves, requiring them to transform into fully digital, online-first organisations. In this context, we present results from research conducted during the second year of PSM-AP, a large-scale comparative research project analysing ‘Public Service Media in the Age of Platforms’. In this brief, we put forward a series of findings and recommendations on PSM publishing and personalisation practices based on analysis of the PSM in-house video-on-demand (VoD) services and main linear channels in the Belgian market, alongside insights from our wider analysis that includes 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".