What's in a Name?: Reflections on Using, Not Using, and Overusing the "G-Word"
Bibliographic record
Abstract
Churchill described the atrocities committed by Nazi troops and police under the German attack on the Soviet Union as something unprecedented: ‘‘Since the Mongol invasions of Europe in the sixteenth century, there has never been methodical, merciless butchery on such a scale, or approaching such a scale. And this is but the beginning. . . .We are in the presence of a crime without a name.’’1 A few years later, at Nuremberg, because of the lack of international legislation on this very crime, Hermann Go¨ring and his cronies were not convicted of genocide against Europe’s Jews or against the Sinti and Roma. The term ‘‘genocide’’ found entry into the language of only some of the indictments. In fact, as is well known to genocide scholars, the term ‘‘genocide’’ had first been coined in a scholarly publication in 1944, too late for the Nuremberg trials, and was introduced to international law only in 1948, when the United Nations adopted the Convention on the Prevention and Punishment of the Crime of Genocide (UNCG).2 This convention was the result of Polish jurist Raphael Lemkin’s tireless lobbying of government representatives from around the world to make genocide an international crime.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".