Bibliographic record
Abstract
In a much-cited recent article, Obermeyer, Murray, and Gakidou (2008a) examine estimates of wartime fatalities from injuries for thirteen countries. Their analysis poses a major challenge to the battle-death estimating methodology widely used by conflict researchers, engages with the controversy over whether war deaths have been increasing or decreasing in recent decades, and takes the debate over different approaches to battle-death estimation to a new level. In making their assessments, the authors compare war death reports extracted from World Health Organization (WHO) sibling survey data with the battle-death estimates for the same countries from the International Peace Research Institute, Oslo (PRIO). The analysis that leads to these conclusions is not compelling, however. Thus, while the authors argue that the PRIO estimates are too low by a factor of three, their comparison fails to compare like with like. Their assertion that there is “no evidence” to support the PRIO finding that war deaths have recently declined also fails. They ignore war-trend data for the periods after 1994 and before 1955, base their time trends on extrapolations from a biased convenience sample of only thirteen countries, and rely on an estimated constant that is statistically insignificant.
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.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".