Bibliographic record
Abstract
No More Killing Fields: Preventing Deadly Conflict, David A. Hamburg, Lanham, Boulder, New York, Toronto, Oxford: Rowman & Littlefield Publishers, Inc., 2004, pp. v, 365 David Hamburg, a physician, scholar, and policymaker, came to think preventively in the 1950s, when he saw the impact of the first polio vaccine. He built on this experience later when, as president of the Carnegie Corporation of New York (1982-1997), he became interested in preventing mass violence—“the prime problem of the twenty-first century” (vii). One of the most important questions he poses in this work is whether it is “beyond human capacity to create secure and decent living standards for people everywhere and to foster just interactions among diverse peoples” (1). Hamburg believes that an evolving worldwide awareness of unprecedented dangers and of equally unprecedented advances in science and technology can help humanity transcend “the ancient habits of blaming, dehumanizing, repressing, and attacking …” (5).
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".