Scarlet Knights, Red Crusade: An Analysis of the Great Red Scare at Rutgers-New Brunswick
Bibliographic record
Abstract
The Paul A. Stellhorn Undergraduate Paper in New Jersey History Award was established in 2004 to honor Paul A. Stellhorn (1947-2001), a distinguished historian and public servant who worked for the New Jersey Historical Commission, the New Jersey Committee (now Council) for the Humanities, and the Newark Pubic Library. An especially active and effective member of the New Jersey history community, he did much to expand the audience for New Jersey history and was an effective advocate for public history and a vigorous supporter of scholarship and publication about the state’s history. As a program officer and a grants administrator he helped many of our present historians and humanities scholars to achieve their goals, whether as scholars, history agency personnel, or educators. He earned a Ph.D. in American History from Rutgers University with a dissertation about Newark during the era of the Great Depression. He was the author or editor of many works about New Jersey’s past, especially about its urban history. The Stellhorn Awards consist of a framed certificate and a modest cash award, presented at the New Jersey Historical Commission’s Annual Conference. The Award’s sponsors are the New Jersey Studies Academic Alliance; the New Jersey Historical Commission, New Jersey Department of State; Special Collections and University Archives, Rutgers University Libraries; the New Jersey Caucus, Mid-Atlantic Regional Archives Conference; and the New Jersey Council for History Education. The Stellhorn Award Committee members are Richard Waldron (chair), Mark Lender, Brooke Hunter, and Peter Mickulas. Click here for more information. The following paper by Mr. Federowicz, nominated by Professor Richard L. McCormick, was one of two 2017 winners.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".