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Record W3042799202 · doi:10.46888/flobamora.v2i1.5

Delapan agenda pembangunan Provinsi NTT tahun 2013-2018 dalam agenda surat kabar

2019· article· en· W3042799202 on OpenAlexaff
Yohanes Museng Ola Buluamang

Bibliographic record

VenueFLOBAMORA · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Administration in Developing Nations
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsNewspaperVictoryTheme (computing)News mediaPolitical scienceNews bureauMedia studiesHeadlineAdvertisingTourismDepictionSociologyPoliticsBusinessLawComputer science

Abstract

fetched live from OpenAlex

The implementation of the eight NTT Province development agendas (2013-2018) received attention in the daily news of Pos Kupang, Timor Ekspress and Victory News. The purpose of this study included identifying the quantity of news from the three newspapers, knowing the description of the news and testing the hypothesis of the different news coverage of three newspapers. The results of the analysis show that the most dominant news theme reported by Pos Kupang daily (71 news), Timor Ekspress (78 news) and Victory News (125 news) is the agenda of the development of populist economy and tourism. The description of the news by the Pos Kupang daily is dominated by neutral portrayals (109 news), Timor Ekspress is dominated by positive depictions (109 news) and Victory News is dominated by negative depictions (180 news). The results of the analysis show that there are significant differences in the news theme and news depiction of the NTT province's eight development agenda for 2013-2018 between the three newspapers. This confirms the importance of an issue that the media considers in determining the media agenda and the way the media express a reality. Keywords: NTT Province Development Agenda, Newspapers, Media Agenda

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
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 head0.040
GPT teacher head0.326
Teacher spread0.286 · 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 designObservational
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
Published2019
Admission routes1
Has abstractyes

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Same venueFLOBAMORASame topicPublic Administration in Developing NationsFrench-language works237,207