Investigating a Japanese Authenticity-Blurring Mechanism in Discourse: “It’s the Mood which has the Last Say in Our Discussion.”
Bibliographic record
Abstract
Every culture has its own repertoire of characteristic discourse patterns. In a discourse, authenticity, which is related to socio-pragmatic strategies, is also culturally influenced. As is often noted, Japanese discourse patterns deviate from the Western norm in that the source of the influential view is intentionally blurred so that it is not easily traceable to its asserters. When the decision process is criticized, people concerned can say, “the mood had the last say in our discussion.” This discourse pattern is referred to as atmospheric dominance. The purpose of this research is to identify sources of the phenomenon through philological research, citing data from the Seventeen-Article Constitution (compiled in 604), Manyousyu (the 8th century anthology of poetry), Kojiki (the oldest chronicle, compiled around the 8th century), and Nihon Shoki (the second oldest chronicle, completed approximately 8th century). Our main point is that the concepts of Wa (harmony) and kotodama (language spirits) pertain to and constitute atmospheric dominance, which are defined with semantic metalanguage. This research clarifies how these two concepts are intertwined and work behind atmospheric dominance by citing documentaries, monologues, and newspaper articles, including the delay of publication of the meltdown incident in 2011.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".