American Multiculturalism and Ethnic Survival
Bibliographic record
Abstract
Contents: Renate von Bardeleben/Sabina Matter-Seibel/Klaus H. Schmidt: Introduction - Juan Bruce-Novoa: The Rhetorics of Latino Survival in the U.S.: 1528-1961 - Vincent Carretta: Revising One's Self: Constructing. Phillis Wheatley's Anglophone-African Identity - Udo J. Hebel: Not a story to pass on? The Pluralization of American Memory in Historical Dramas by Harlem Renaissance Women Playwrights - Charles T. Johnson: Dr. Charles J. Hexamer: Forgotten German-American Leader - Barbara Buchenau: Ethnic Performance and the Self-Representation of Frederic Philip Grove - Laura Hapke: Red Scares, Ethnicity, and the Jungle of Worlcing-Class Desire in the 1920s Novels of Theodore Dreiser and Upton Sinclair - Elke Kinkel: Thomas Mann in America: A Privileged German's (Ethnic) Survival - Alvin H. Rosenfeld: Translating Atrocity - Harald Leder: Changing People's Minds? American Reorientation in Germany After World War II - John Purdy: Autoethnography and Issues of Representation in the Post-Indian Era - Sabina Matter-Seibel: Aliens, Translators, Native Speakers: Language and Identity in Asian-American Fiction - Maren Dingfelder Stone: Tulips, Windmills, and Wooden Shoes: Dutch-American Festive Culture as a Means of Ethnic Survival? - Horst Tonn: Ethnic Interventions: Hollywood Film and the New Multiculturalism - Justine Dymond: Ethnic Mysteries: Genre, and the Power of Law - Edgars Osins: Multicultural Canada: Two First Nations Voices - Suzanne Ferriss: Hybrid Subjectivities: Sexuality, Race, and Ethnicity.
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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 | 0.001 | 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 | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".