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Record W4404423884 · doi:10.1177/13678779241296556

People, personalisation, prominence: A framework for analysing the PSM shift to digital portals and interrogating universality across contexts

2024· article· en· W4404423884 on OpenAlexaboutno aff
Catalina Iordache, Daniel Martin, Julie Mejse Münter Lassen, Tim Raats, Filip Świtkowski, Katarzyna Gajlewicz‐Korab, Catherine Johnson

Bibliographic record

VenueInternational Journal of Cultural Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsnot available
FundersArts and Humanities Research CouncilHorizon 2020 Framework Programme
KeywordsPersonalizationSociologyPublic relationsDigital mediaQualitative researchContext (archaeology)Universality (dynamical systems)Political scienceBusinessWorld Wide WebMarketingSocial scienceComputer science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.069
GPT teacher head0.466
Teacher spread0.397 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2024
Admission routes1
Has abstractyes

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