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Record W4380980054 · doi:10.55849/jidc.v2i1.106

Analysis of Radio Broadcast

2022· article· en· W4380980054 on OpenAlexaff
Silviana Ramadhani, Raihana Ataqiya, Bakti Haris, Eric Law, Jey Royi

Bibliographic record

VenueJournal International Dakwah and Communication · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Finance and Communication
Canadian institutionsConcordia University
Fundersnot available
KeywordsBroadcasting (networking)Radio broadcastingEntertainmentRadio programComputer scienceBroadcast engineeringMultimediaAdvertisingPublic broadcastingGraphicsTelecommunicationsComputer networkBusinessComputer graphics (images)Political science

Abstract

fetched live from OpenAlex

Radio is a public media that conveys the content of its message in the form of information, entertainment, news, and education by audio. In broadcasting, the Radio should have a program that suits the tastes of listeners, has excellent programs, and broadcasters who have their own privileges in communicating with their listeners, as is the case with this Fuad FM radio with some of its program programs that adjust the tastes of its listeners, besides that it does not forget to make its flagship show programs. Therefore, the purpose of this study is to analyze radio broadcast programs in general. The motto used is a qualitative research method. The results obtained from this study show an in-depth analysis of the ins and outs of radio broadcasting, starting from the programs, strategies for creating programs, factors, and others. So, a broadcast program is everything that a broadcasting station displays in the form of sound, image, or sound and image or in the form of graphics, and characters, whether interactive or not, that can be received through a broadcast receiving device to meet the needs of its audience.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.006
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.020
GPT teacher head0.321
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations5
Published2022
Admission routes1
Has abstractyes

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