L’usage des stéréotypes nationaux et ethniques et la structuration des dialogues interculturels dans <i>Fußnoten</i> de Nacha Vollenweider et <i>Im Land der Frühaufsteher</i> de Paula Bulling
Bibliographic record
Abstract
This article compares the use of statements and assertions that rely heavily on stereotypes in Paula Bulling’s Im Land der Frühaufsteher (2012) and Nacha Vollenweider’s Fußnoten (2017). Through theories of cross-cultural communication, this essay draws attention to the relationship between stereotypes and the organization of conversations and dialogues. I thereby demonstrate how the foreign characters in both graphic novels—a Malian asylum seeker in Bulling’s graphic novel, and an Argentine immigrant who does not yet have the “dauerhafte Aufenthaltsgenehmigung” in Fußnoten—are more capable than local Germans in working through disturbances in cross-cultural communication, allowing for the conversation to move forward. This essay therefore reveals how these graphic novels either completely subvert common existing stereotypes about Black Africans or encourage a reassessment of the alleged openness of Germany in an era of global migration and displacement, while empowering immigrants and asylum seekers by illuminating their cross-cultural competence in daily conversations.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".