Denise Bukowski, Denise Bukowski and the Antiwar Migration
Bibliographic record
Abstract
[profile bio] Denise Bukowski is a book editor and literary agent at the Bukowski Agency in Ontario, Canada. She was born and raised in New York, and went to college at Boston University. Going to school and living in the hotbed of antiwar activity that was the city of Boston, Denise became an avid participant in the antiwar movement. During her third year of college, she studied overseas at Leeds University in England. There she met the man for whom she would move to Ontario in 1970 to avoid an unfair draft. Although she retains her U.S. citizenship, she has been there ever since. [profiler bio] Ayush Garg is a Sophomore undergraduate student in USC Viterbi School of Engineering studying Electrical Engineering. He is from New Delhi, India.; Zi Wang is a senior undergraduate student in USC Marshall School of Business. He is from Beijing, China.; Anita Wang is a sophomore undergraduate student in USC's Price School of Public Policy. Her degree includes a focus in health policy and management. She is from Pasadena, California.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.071 | 0.090 |
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".