Contemporary Cultural Representations of Japans Education-Credential Society and the Affect of Disgust
Bibliographic record
Abstract
In this article, I examine and critically assess representations of Japans neoliberal-era education-credential society (gakureki shakai) in popular culture. First, I explore the manga Dragon Zakura (Doragon zakura, 2003-2007), in which a non educator is entrusted to revive a financially ailing private high school by creating a specialized class preparing students to qualify for the University of Tokyo. This story reflects the reality of Japanese society in the 2000s, characterized by a decreasing student-age population—pushing private schools into financial crisis and leading to decline in student performance—and employment instability under neoliberalism. Second, I examine the novel based on true events Because Shes Brainless (Kanojo wa atama ga warui kara, 2018), exploring its depiction of how university students deviation score (hensachi) continues to impact their lives even after becoming university students, and its legitimization of meritocracy. Through these analyses, I show how the signifier known as the University of Tokyo is being consumed in Japan as a buffer against, and palliative for, the exhaustion of living in a society constantly demanding the proof of ones abilities through unlimited competition. Finally, I explore some recent critically acclaimed novels portraying the lives of women inhabiting a world where neither education credentials nor deviation scores have any significance. In criticizing the heritability of education credentials, these works evince not only a division in representations of Japans education-credential society but also deep inequality irresolvable through the expansion of educational opportunities alone. This suggests the need to approach the issue of education-credential inequality by confronting the human vulnerability that persists beyond the reach of increased educational opportunities.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.032 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".