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Record W4381682832 · doi:10.24434/j.scoms.2023.03.3708

Television archives, social networks and the young audiences: The example of Internet memes as a way to revitalise public broadcasters’ engagement

2023· article· en· W4381682832 on OpenAlexfundno aff
Juan Francisco Gutiérrez Lozano, Antonio Cuartero

Bibliographic record

VenueStudies in Communication Sciences · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicRadio, Podcasts, and Digital Media
Canadian institutionsnot available
FundersYork University
KeywordsAgency (philosophy)The InternetContent analysisSociologyMedia studiesSocial mediaAdvertisingPublic relationsPolitical scienceWorld Wide WebSocial scienceComputer scienceLawBusiness

Abstract

fetched live from OpenAlex

The purpose of this research is to explore the efforts that Spanish public television archives are making to bring their audiovisual content closer to young people via social networks. Specifically, this text focuses on analysing the public television archives of the Spanish national television company RTVE and of the Andalusian regional public television agency (RTVA), known as Archivo RTVE and MemorANDA, respectively. The methodology used is based on both qualitative and quantitative tools, consisting of content analysis and in-depth interviews. The results obtained show that both platforms manage to reach young people, but indirectly through viral videos or Internet memes. The RTVE archive is the most successful among young people because it has a more extensive collection and more resources as well as a policy that is more clearly geared towards the dissemination of its audiovisual heritage. The most negative aspect identified in this study was the repetition of regional clichés, especially in the case of MemorANDA.

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)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.008
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.253
GPT teacher head0.421
Teacher spread0.168 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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