Bibliographic record
Abstract
Over the last several decades, scholars have lamented the lack of a clear definition of genocide, and many have then proposed one of their own in an attempt to fill this gap. The majority of the definitions put forward, and the discomfort from which they arise, express an explicit or implicit attachment to essentialism, the assumption that the definition need reflect, in an unadulterated fashion, the objective reality out in the world. In other words, many take our failure to settle on a single definition of genocide across fields as a sign that we don't yet know what it is. We seem to want to know not so much what genocide means to us and how this construct might be more useful in our attempts to create a better world, but rather, what it really is at its core. In what follows, we will examine our ongoing attachment to essentialist understandings of genocide, some of the problems that arise as a result, how we have attempted to free ourselves from this approach, and how we might go further in doing so.
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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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.089 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".