Bibliographic record
Abstract
I consider myself a fortunate one to have both Peter Rachleff and David Roediger, great historians of race and labor, as my mentors.This fortuitousness, I have come to appreciate, has much to do with the community of thinkers, writers, and activists from which both Peter and Dave came that I found nearly two decades ago.Upon entry, I latched onto the ways of studying and writing U.S. history tightly bound up with "history making." I am deeply indebted to them for their visions, advice, close reading, utmost sensitivity toward the writer's craft, and above all unwavering solidarity.Equally pivotal to my intellectual development were Jeani O'Brien, Erika Lee, and Ted Farmer.Their enthusiasm kept me above water while I completed my dissertation at the University of Minnesota, and their feedback on my work came without missing a beat to help me achieve analytical sharpness.Through the years, funding for research came from multiple sources at the University of Minnesota: the Interdisciplinary Center for the Study of Global Change; the Race, Ethnicity, and Migration Seminar; the Department of History; the Program in Asian American Studies; the Office of Equity and Diversity; and the College of Liberal Arts.Also vital were the research award from the Professional Staff Congress at The City University of New York and participation in the Network Summer Faculty Enrichment Program at New York University, specifically the seminar titled Modern Jazz and the Political Imagination convened by
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.340 | 0.241 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".