Romanticizing Difference: Identities in Transformation after World War I
Bibliographic record
Abstract
T his special issue explores the theme of essentialist discourses about lan- guages, human collectivities, and human diversity during the interwar years, outside of explicitly racist or antisemitic perspectives.It grew out of a conference that Cécile Mathieu and I organized at the Université de Picardie Jules Verne in October 2013.Our original inspiration came from the discovery of thematic overlap in our research on the 1920s in our two quite disparate fi elds of history and linguistics.In particular, we were both struck by the contrast between contemporary associations of essentialized representations of human collectivities and practices of oppression, discrimination, and genocide, and the prevalence of these kinds of discourses across the political and ideological spectrums in the pre-World War II era.In our post-Holocaust and postcolonial world, progressive politics and an understanding of differences between human collectivities rooted in unchangeable biological realities do not marry well together.In an earlier period, in which theorizing about human difference was not associated with genocide and European colonialist domination was assumed by all but a few outliers to be the natural and rightful order of things, the borders between universalism, humanism, romanticism, and racism were much messier and intertwined.This volume brings together researchers in history, linguistics, and literary studies to refl ect on these issues in order to explore the varied ways in which human difference was conceived of in the interwar years.We particularly emphasize the impact of World War I and the ideological and political shifts of the 1920s.World War I overturned existing systems of beliefs and values in the Western world, highlighting a growing cultural malaise.While belligerent nationalist discourses were undeniably prominent during this period, other discourses, founded on new hopes and dreams, were rooted in the desire to promote both mutual coexistence and a respect for difference.The fall of the Austro-Hungarian and Ottoman Empires permitted a dozen small nations to make their dream of self-determination come true, and the creation of the League of Nations in 1920 was a part of this dream of international cooperation and harmony within a nationalist framework.Although this
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".