People, personalisation, prominence: A framework for analysing the PSM shift to digital portals and interrogating universality across contexts
Bibliographic record
Abstract
In the context of enhanced platformisation, Public Service Media (PSM) are once again forced to rethink the ways in which they achieve core public values. To this end, PSM have been prioritising the development of their own video-on-demand portals. To contribute to ongoing research, we propose a theoretical framework that can be applied by future PSM work, based on the operationalisation of platformisation in PSM policy documents and strategy. We identify the shared priorities across ten media organisations in seven media markets: Belgium—Flanders and Wallonia-Brussels, Canada, Denmark, Italy, Poland, and the UK. The study is based on the qualitative analysis of 61 documents, outlining the PSM remit and how they report and present themselves to governments, collaborators, and audiences, contextualised by ongoing national and regional debates. Findings confirm that the framework of people, personalisation, and prominence can serve as a useful theoretical basis for understanding and interrogating universality across contexts.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.007 | 0.063 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".