Barriers to vehicle-sharing among NGOs during disaster relief operations: Findings from a developing country's perspective
Bibliographic record
Abstract
NGOs (Non-governmental Organizations) are still lagging in adopting the benefits of vehicle-sharing during disaster relief operations. Therefore, the primary objective of this investigation is to scrutinize the impediments that impede the cooperative efforts among NGOs in the context of vehicular resource sharing within the framework of disaster relief operations. The idea is to enhance relief operations' transport capacity, reduce pollution and congestion, and benefit the total environment without increasing the number of vehicles. In order to study this little-known area of research, an exploratory study is conducted by interviewing experts (i.e., those responsible for administering relief distributions) from both local and international NGOs in a regularly disaster-troubled developing country, Bangladesh. Using the innovation resistance theory, this study examines and categorizes a list of potential important barriers. In this study, various types of barriers are identified based on the innovation resistance theory. These barriers are categorized into five groups, including risk (consisting of 10 barriers), usage (comprising of 7 barriers), tradition (including 4 barriers), value (comprised of 2 barriers), and image (consisting of 1 barrier). Accordingly, vehicle-sharing is linked with many potential side-effects or uncertainties, as the majority of the identified barriers are connected to risks. It is also worth noting that certain vehicle-sharing barriers, which may seem crucial, can be addressed for better outcomes. For example, a strategy could be to communicate potential legal barriers and predetermine accountability for possible accidents. The research outcomes also suggest that the implementation of a vehicle-sharing initiative within the realm of disaster relief operations is susceptible to a multitude of intricate and technically oriented usage challenges that are inherently unavoidable. Notable among these issues are the physical characteristics of the vehicles, the potential for complications arising from the commingling of similar relief items, instances of vehicular overloading, and encounters with roadblocks along the designated routes. The findings have implications for HOs (Humanitarian Organizations), logistics service providers, as well as disaster preparedness, response, and recovery professionals.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".