The Daily Us (vs. Them) from Online to Offline: Japan's Media Manipulation and Cultural Transcoding of Collective Memories
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".