Defining Whiteness: Race, Class, and Gender Perspectives in North American History
Bibliographic record
Abstract
African-American writers such as W. E. B. Du Bois, James Baldwin, and Ida B. Wells have regarded “whiteness” as a problem for a long time. However, it is only fairly recently that white historians have taken seriously the importance of de-naturalizing “whiteness,” and critically examining its privileges. “Defining Whiteness: Race, Class, and Gender Perspectives in North American History,” was sponsored by the University of Toronto and York History Departments, the Centre for the Study of the United States, and the Centre for Ethnic and Pluralism Studies at the University of Toronto, with the cooperation of International Labor and Working-Class History and the Canadian Committee on Labour History and its journal Labour/Le Travail. Conference organizers invited several leading American scholars of “whiteness” to Toronto, where they, along with a number of Canadian scholars, presented papers on the ways that whiteness has been constructed in North America. The conference contained much to interest labor historians and those interested in class/race/gender analytical frameworks.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.036 | 0.035 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".