Bibliographic record
Abstract
PURPOSE: There are many differences in how authorities handle the dead during mass death incidents involving disasters and pandemics. These differences would suggest that planning for a disaster death and planning for a pandemic death should be done separately. This may be true to some extent, however, there are many similarities between the two that this article will seeks to examine. The main objective of this study is to show that planning for both disasters and pandemics should either be done by a single entity that coordinates both types of responses, or by agencies that communicate closely and frequently. DESIGN/METHODOLOGY/APPROACH: This study compared mass death incidents predominantly within the Canadian historical record, including disasters and pandemics. It took a specific look at the influenza pandemic of 1918 in North America and how the dead were handled. FINDINGS: Both disasters and pandemics offer unique challenges in handling the dead and documenting the incident. In a pandemic the cause of death is usually clear, while in a disaster it is not always understood. However, the similarities they hold in common must not be overlooked. They will involve immense and complicated amounts of paperwork, cause a shortage of supplies (be it medical, food or otherwise) and create the need for assistance. ORIGINALITY/VALUE: The research finds that though disasters and pandemics are often handled differently by the various agencies involved, they should be treated alike and dealt with in the same manner.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".