Assessing Bias: The Qualitative in the Quantitative, Darfuri War Fatalities and the Morality of War
Bibliographic record
Abstract
This paper formulates a strategy for assessing bias, and applies it to quantitative assessments of the disaster of war in Darfur [Sudan]. In so doing it argues for qualitative investigations of quantitative analyses. The strategy examines epistemic and political regimes with the goal of revealing the sources, the directions, and the forces of bias. Examples of bias are discussed to illustrate the strategy including, among others, the draw-a-person IQ test, questions about how old you are or whether you can bear children in Chad, and the US army’s Human Terrain System. Considerable attention is paid to US governmental biasing of its claims of war fatalities and genocide in Darfur. This biasing is shown to involve cherry picking, symbolic violence, and high-channel regimes of bias. It is shown how the bias assessment strategy may be of use in evaluating moral claims.
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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 | 0.122 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.008 | 0.051 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".