Bibliographic record
Abstract
“An important addition to the very limited and fragmented body of work related to the subject of atrocities in our time. Well done.” General (Retired) Gordon R. Sullivan, US Army, Former Chief of Staff of the US Army “Sarah Sewall and her team have produced an impressive contribution that shifts the debate on intervention from ‘whether ’ to ‘how. ’ With this critical recognition that mass atrocities present unique operational challenges, ‘MARO ’ is a step closer to incorporation into military doctrine. National governments and the international community badly need the MARO framework as an effective template for ‘soft ’ and ‘hard ’ power responses before, during, and after preventable mass atrocities are committed.” Senator (Retired Lieutenant General) Roméo A. Dallaire, Canadian Forces, Former Force Commander United Nations Assistance Mission for Rwanda “The MARO Project provides a solid framework for Geographic Combatant Commander development of contingency plans for mass atrocity situations. Tailored ‘on the shelf ’ plans would be
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.462 | 0.296 |
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".