The European Public Sphere: European Commission Initiatives on Creating Transnational Media Networks
Bibliographic record
Abstract
The results of the European Parliament Elections 2014 demonstrated a rise of Euroscepticism in the EU.The reasons of this are a lack of information about the EU in the Member States and insufficient mechanisms for debate and citizens' engagement in decision making.The analysts explain it also by the weakness of providing a forum for the development of common identity and legitimacy of the European public sphere.This thesis looks at media as mechanisms of the European public sphere.The thesis examines institutional models of two transnational audiovisual media on European affairs supported by the European Commission: the Euronews television and the Euranet Plus radio network.The author considers the roles of Euronews and Euranet Plus in the European public sphere and in attempting to reduce Euroscepticism.The thesis contains a comparative analysis of the two models and shows how the experience with Euronews has affected the development of Euranet Plus.The thesis makes use of interviews with representatives of the European Commission, media in Belgium and France and a field study at the editorial office of Euronews in Lyon-Ecully and at the editorial offices of Euranet Plus in Paris and Brussels that took place in May 2014.This research would not have been possible without the ongoing encouragement and outstanding support from my supervisor, Professor Joan DeBardeleben.My deepest appreciation goes to Professor DeBardeleben
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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 | 0.025 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".