Exporting the Public Value Test: The Regulation of Public Broadcasters’ New Media Services Across Europe
Bibliographic record
Abstract
Public service broadcasters across Europe are venturing into the digital world, launching niche TV channels, building extensive websites, developing commercial services, entering into partnerships with external actors, and exploring new ways to reach users, whether its through smart phone apps or screens in public spaces. Such endeavours intensify fundamental discussions about what we need public service media institutions for. These are complex discussions, building on history, encompassing new technology, and involving a range of strong stakeholders. Recently, the so-called public value test has emerged as the focal point for these discussions. As a detailed regulatory scheme to measure the public worth and possible market impact of planned publicly funded media services, the public value test is causing controversy across Europe. This collection of short essays from academics, regulators, public broadcasters and private media representatives, provides thought-provoking perspectives on the state of play of public value tests in a range of European states. In so doing, the book is a topical intervention in the ongoing debate about the future of our media systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.021 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".