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Record W3103692323 · doi:10.6000/1929-4409.2020.09.128

Radio Broadcasting Experience and Development Prospects (European Scientific Dialogue)

2020· article· en· W3103692323 on OpenAlexvenueno aff
Vadim A. Ryazantsev, Andrey V. Polonskiy, Irina I. Karpenko, Elena I. Radchenko, Svetlana M. Narozhnyaya

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicRadio, Podcasts, and Digital Media
Canadian institutionsnot available
Fundersnot available
KeywordsBroadcasting (networking)Radio broadcastingFocus (optics)Mass mediaTelecommunicationsAdvertisingMultimediaComputer scienceBusinessComputer security

Abstract

fetched live from OpenAlex

The paper presents an analysis of various opinions concerning radio-industry development issues, given new IT processes implemented in mass media. The authors study the technological evolution of radio broadcasting in terms of analyzing its main development stages. They provide an assessment of listeners’ high confidence in radio broadcasts as compared to other mass media sources. Focus is placed on promoting sonic brands in the sphere of media-communication. The researchers also pay special attention to podcasting as a new way to listen to audio-content. The advantages and possible consequences of podcasting influence on radio-industry are assessed. The research is based on the expert discussion on radio broadcasting issues which took place at “Radiodays Europe 2019”.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
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 head0.116
GPT teacher head0.330
Teacher spread0.214 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

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