Bibliographic record
Abstract
Abstract Germans working in Austria are confronted with several subliminal resentments. Research on nationalism, racism, and diversity has overlooked this topic up to now, as Germans, as well as other Northern American or Western European citizens, are very rarely analysed as marginalized groups. Furthermore, the situation of migrants from geographically, linguistically, and culturally close countries, has received scant attention and been deemed of little importance up to now. The anti‐German sentiments in Austria are primarily based on the Austrian self‐perception of being non‐German as a constitutive element of ‘Austrian‐ness’. Related to that, negative attributions ascribed to Germans simultaneously mean a positive attribution of ‘Being Austrian’. Based on a content analysis of ten narrative interviews, conducted with Germans working in Austria, it appears that they permanently experience being categorized as ‘the Germans’, which leads to several types of exclusion, demotion, and also discrimination in the workplace. Differences in hierarchy level and perceived competition seem to moderate this effect. It appears that workplace superiors of Germans tend also to put an emphasis on positive stereotypes related with Germans, whereas Austrian colleagues on the same hierarchy level tend to performatively construct and reproduce ‘Being German’ as a deficit that they associate with negative stereotypes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".