Right Angles: Examining Accounts of Japanese Neo-nationalism
Bibliographic record
Abstract
23 July 2007 a video short attached an online article in the New York Times proclaimed is asserting itself militarily. It is embracing right-wing nationalism. It is denying its wartime atrocities. And it is flirting with nuclear weapons.1 While the tone of the short sometimes bordered on the histrionic, its conclusions were consistent with a general trend in recent observations about social and political movements in Japan. Speculation about a popular shift the right in Japanese public opinion is currently rife in many overseas media outlets and academic journals. Japan's leaders are portrayed as mindful of a growing nationalist sentiment2 and keen abolish pacifist provisions in the nation's Constitution in order to make Japan permit war.3 Meanwhile, popular expressions of nationalism are found in comics and movies, in an enthusiasm for World Cup soccer, in an indignation over North Korean missiles and, far from least, in Japan's anxiety over China's emerging influence.4 [Nationalism, according Michael J. Green, a former US National Security Council director for Asian Affairs, characterizes the [Japanese] public mood these days.5 This focus on Japan's resurgent right-wing nationalism left many commentators unprepared explain the crushing defeat suffered by the Liberal Democratic Party in the 29 July 2007 Upper House election. In the space of a few weeks, the press went from describing the now former prime minister as a man leading Japan toward a new nationalism talking about
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.025 | 0.022 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".