Bibliographic record
Abstract
Since the early 2000s, Japan has witnessed the growing salience of so-called netto uyoku (online right-wingers). This group is characterized by strong anti-China and anti-Korea sentiments, nationalistic political views, and online political engagement. While online radical right movements in Europe are often regarded as support bases for radical right candidates or parties, few studies have investigated whether this assumption applies to Japanese online right-wingers. The present study sought to shed light on this issue by conducting a large-scale web survey with 77,084 respondents living in the greater Tokyo metropolitan area. Respondents were registered monitors for a research company. The large sample size enabled multivariate analyses to clarify the characteristics of online right-wingers in comparison to other respondents. The results indicated that 1.5 percent of the respondents (1,167) could be classified as online right-wingers and, in contrast with the political discontent hypothesis, most online right-wingers had a high degree of external political efficacy and lower levels of populist attitudes relative to other respondents. This suggests that online right-wingers trusted and remained satisfied with the current political conditions. Furthermore, they were more likely to vote, especially for established conservative parties, suggesting an affinity among online right-wingers for traditional conservative parties and candidates. These findings indicate that, unlike in Europe, Japanese online right-wingers are not a support base for radical right candidates and parties. In Japan, which is under a right-leaning government, online right-wingers have not become a driving force for emergent radical right parties.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".