Deutsch-chinesische Helden und Anti-Helden
Bibliographic record
Abstract
This volume elucidates the changing relationship between heroization and othering in a German-Chinese cultural comparison. Intercultural case studies illustrate which representatives of German culture and history were subjected to a process of heroization or were disparaged as negative anti-heroes in Chinese culture. Vice versa, Chinese figures who adopted a corresponding heroic or antiheroic function within the German-speaking world are also examined. This German-Chinese dialogue, in which cultural scientists from Germany and China participate, is guided by the assumption that processes of heroization and de-heroization represent paradigmatic focal points in the economics of intercultural transfer. The relationship between individual and collective heroism and the meaning of alienness - be it of Chinese or German characteristics - when importing heroes offer new perspectives insofar as these importations prove to be complex and inconsistent. With contributions by Achim Aurnhammer, Chen Zhuangying, Cong Tingting, Fan Jieping, Olmo Gölz, Joachim Grage, He Zhiyuan, Huang Liaoyu, Hu Chunchun, Hu Kai, Sara Kathrin Landa, Stefanie Lethbridge, Lin Chunjie, Dieter Martin, Isabell Oberle, Dominik Pietzcker, Nicola Spakowski, Jennifer Stapornwongkul, Wang Zhiqiang, Wei Yuquing, Xie Juan, Zhang Fan, Zhu Jianhua, Ulrike Zimmermann.
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 | 0.001 | 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 | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".