Bibliographic record
Abstract
Who is white, and why should we care? There was a time when the immigrants of New York City’s Lower East Side—the Irish, the Poles, the Italians, the Russian Jews—were not white, but now “they” are. There was a time when the French-speaking working classes of Quebec were told to “speak white,” that is, to speak English. Whiteness is an allegorical category before it is demographic. This volume gathers together some of the most influential scholars of privilege and marginalization in philosophy, sociology, economics, psychology, literature, and history to examine the idea of whiteness. Drawing from their diverse racial backgrounds and national origins, these scholars weave their theoretical insights into essays critically informed by personal narrative. This approach, known as “braided narrative,” animates the work of award-winning author Eula Biss. Moved by Biss’s fresh and incisive analysis, the editors have assembled some of the most creative voices in this dialogue, coming together across the disciplines. Along with the editors, the contributors are Eduardo Bonilla-Silva, Nyla R. Branscombe, Drucilla Cornell, Lewis R. Gordon, Paget Henry, Ernest-Marie Mbonda, Peggy McIntosh, Mark McMorris, Marilyn Nissim-Sabat, Victor Ray, Lilia Moritz Schwarcz, Louise Seamster, Tracie L. Stewart, George Yancy, and Heidi A. Zetzer.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.010 |
| Insufficient payload (model declined to judge) | 0.029 | 0.015 |
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".