Mass Fatality Management: Issues in Identifying and Repatriating Following Mass Fatalities Abroad
Bibliographic record
Abstract
Globalization has led to increases in foreign nationals, including Canadians, perishing abroad as a result of a mass fatality event (MFE).Despite ongoing efforts to establish comprehensive mass fatality management plans (MFMP), there isn't an effective "one-size-fits-all" model.Due to the complexity and numerous factors following MFEs, there is a need for ongoing innovation regarding MFMP.I will use the New Haven School of International Law decision-making model as it provides a structured framework in which previous MFM decisions can be appraised, and proposals can be made.Following the analysis of three MFE case studies, I will provide recommendations regarding mass fatality management (MFM), with specific considerations paid to the various legal issues surrounding jurisdiction over the deceased.I will analyze decisions made by international actors regarding recovery and the forensic identification of deceased following these three events in an effort to illustrate the requirement for flexible and agile MFMP.Applying New Haven Framework to the Haiti Earthquake…………………..….65 Haiti Earthquake: Step One: Goal Formation/Problem Identification…...66 Haiti Earthquake: Step Two: Identification of Conflicting Claims/Controversies………………………………………………….....70 Haiti Earthquake: Step Three: Analysis of Past Trends in Decisions……74 Haiti Earthquake: Step Four: Projection of Future Trends………………75 Haiti Earthquake: Step Five: Conclusion/Recommendations……………77 Applying New Haven Framework to the 2001 Twin Tower Attacks……78 Applying New Haven Framework to the Twin Tower Attacks………………….79
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".