Platform paradoxes and public service media legitimacy: a cross-national study
Bibliographic record
Abstract
The intricate relationship between public service media (PSM) and social media platforms has emerged as a critical factor significantly impacting the legitimacy of the PSM institution. This study adopts a discursive institutionalism lens to examine how six PSM organizations across Europe, Australia and Canada communicate their relationship and ideas about social media platforms via their annual reports over a 10-year period (2013–2022). Annual reports provide valuable insight into PSM organizations’ discursive processes that are aimed at generating (and justifying) public and political support. The analysis uncovers a complex and at times contradictory set of discourses revolving around audience attention and interaction, editorial integrity, and digital safety. While building platform presence is portrayed as crucial for sustained reach and relevance, PSM organizations also position themselves as counterweights to negative platform influences within the national media ecology. We observe a shift towards increasingly risk-oriented platform narratives over time, particularly concerning Facebook, resulting in a more deliberate social media strategy among some PSM organizations, and even disengagement with the platform.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".