How to estimate (and not to estimate) war deaths: A reply to van Weezel and Spagat
Bibliographic record
Abstract
Stijn van Weezel and Michael Spagat (2017) have critiqued our 2011 report of mortality in Iraq following the 2003 US-led invasion in this issue of Research & Politics. In this response, we make our case for reporting both direct and indirect excess war-related deaths (while distinguishing the difference), defend our efforts to account for survival bias, and provide evidence for including all household-reported deaths, not just those cases where a death certificate can be demonstrated. We also point out Van Weezel and Spagat’s misunderstanding of our sample selection method, despite our citation of our separate paper that thoroughly describes our approach.
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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 | 0.112 | 0.375 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.007 | 0.019 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.034 | 0.099 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".