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Record W3011260261 · doi:10.17477/jcea.2019.18.2.049

The Daily Us (vs. Them) from Online to Offline: Japan's Media Manipulation and Cultural Transcoding of Collective Memories

2019· article· en· W3011260261 on OpenAlexaff
Midori Ogasawara

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Culture and Media Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsTranscodingOnline and offlineComputer scienceComputer networkOperating system

Abstract

fetched live from OpenAlex

Since returning to power in 2012, the second Abe administration has pressured Japanese mainstream media in various ways, from creating the Secrecy Act to forming close relationships with media executives and promoting anti-journalism voices on social media. This article focuses on the growth of a jingoist group called the 'Net-rightists' ('Neto-uyo' in the Japanese abbreviation) on the Internet, which has been supporting the right-wing government and amplifying its historical revisionist views of Japanese colonialism. These heavy Internet users deny Japan's war crimes against neighboring Asian countries and disseminate fake news about the past, which justifies Prime Minister Shinzo Abe's hostile diplomatic policies against South Korea and China. Over the past years, the rightist online discourses have become powerful to such an extent that the editorials of major newspapers and TV reports shifted to more nationalist tones. Who are the Neto-uyo? Why have they emerged from the online world and proliferated to the offline world? Two significant characteristics of new media are discussed to analyze their successful media manipulation: cultural transcoding and perpetual rewriting of collective memories. These characteristics have resulted in constructing and reinforcing the data loops of the 'Daily Us' versus Them, technologically raising current diplomatic tensions in East Asia.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.251
GPT teacher head0.530
Teacher spread0.278 · 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 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

Citations1
Published2019
Admission routes1
Has abstractyes

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