Assessing Bias: The Qualitative in the Quantitative, Darfuri War Fatalities and the Morality of War
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.009 | 0.002 |
| 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 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it