Bibliographic record
Abstract
As the scale and frequency of natural disasters and other emergencies continues to rise in Canada, the Canadian Armed Forces (CAF) has increasingly taken part in domestic disaster assistance operations. Operation LENTUS, being the CAF’s name for all domestic natural disaster assistance operations, has seen the deployment of thousands of CAF personnel over recent years. The same is true for Operation LASER and VECTOR—the CAF’s operations in support of COVID-19 mitigation and vaccination efforts respectively. As such, emergency management (EM) practitioners are increasingly interacting with CAF personnel, both in headquarters environments for operational planning as well as in field conditions during the execution of specific EM tasks. Despite this, and by no fault of their own, many EM practitioners are unfamiliar with the CAF and are subsequently unsure how best to integrate CAF resources into their operations. Due to the complex nature of interagency EM operations, fostering mutual understanding and awareness is crucial to conducting effective EM operations. As such, this paper seeks to bridge this gap in the general knowledge of emergency managers towards their CAF partners during domestic operations. In doing so, this paper will explain and discuss various aspects of the CAF in domestic operations across the complete spectrum of operations, from legislative/strategic considerations to aspects of local execution of EM tasks. These explanations will serve as a starting point for emergency managers to improve their understanding of the CAF and guide their considerations when working in partnership with the CAF. It is hoped that bridging this gap will improve the operational effectiveness of CAF-Civil authority interagency operations and subsequently benefit Canadians in their times of need.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.084 | 0.062 |
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".