Bibliographic record
Abstract
According to Canada’s Incident Command System (ICS), logistics represent the provision of “resources and other services to support incident management” (ICS Canada, 2019). Sometimes referred to as the “getters”, people working in a Logistics Section are faced with the challenge of finding diverse products and services to support efforts on the front lines, within the command centre, and at reception and registration centres. While a comprehensive hazard assessment can be helpful to identify typical resources, in a disaster situation, we never really know what we need until we need it – and then we need it right away. According to Young (2014), “the logistics and resource management functions of Emergency Management (refer to as EM logistics) have been largely reactive, with little to no pre-event planning for potential demand”. In other words, capacity building is absent. Where logistics planning has been made a priority, it generally appears in the form of updated contact information and vendor lists painstakingly collated annually. This results in a labour-intensive, redundant, and highly ineffective process and outcome. In the constantly changing and dynamic environment of emergency management, maintaining current information is a significant challenge for even the most advanced organizations. Many organizations conduct emergency management planning and preparations from the corner of their desk as they lack the capacity and resources to dedicate full time attention. Directors of Emergency Management (DEMs) regularly build relationships within their sector and engage “contractors through personal relationships and other channels” (KPMG, 2021. P.130). However, major disaster situations remove the DEM from significant logistics functions, leaving other members of the local EM team to fulfil the Logistics responsibilities. Incomplete paper or digital lists with missing information add time and complexity that can result in missed opportunities to mitigate damage to people and property – especially when the logistics functions are being undertaken by people infrequently engaged in crisis situations. This article substantiates the need for logistics as a central consideration in building and maintaining networks for connection and collaboration, identifies the value of maintaining comprehensive resource lists as a logistics function, and highlights the significance of building and maintaining local data as part of a solid strategy in preparation for the next disaster situation.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.301 | 0.233 |
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".