Knowledge-Based Issues for Aid Agencies in Crisis Scenarios
Bibliographic record
Abstract
As part of its expanding role, particularly as an agent of peace building, the United Nations (UN) actively participates in the implementation of measures to prevent and manage crisis/disaster situations. The purpose of such an approach is to empower the victims, protect the environment, rebuild communities, and create employment. However, real world crisis management situations are complex given the multiple interrelated interests, actors, relations, and objectives. Recent studies in healthcare contexts, which also have dynamic and complex operations, have shown the merit and benefits of employing various tools and techniques from the domain of knowledge management (KM). Hence, this paper investigates three distinct natural crisis situations (the 2010 Haiti Earthquake, the 2004 Boxing Day Asian Tsunami, and the 2001 Gujarat Earthquake) with which the United Nations and international aid agencies have been and are currently involved, to identify recurring issues which continue to provide knowledge-based impediments. Major findings from each case study are analyzed according to the estimated impact of identified impediments. The severity of the enumerated knowledge-based issues is quantified and compared by means of an assigned qualitative to identify the most significant attribute.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".