Local planning responsibilities for disaster waste management (DWM): Building knowledge from storm Alex in the South Region of France
Bibliographic record
Abstract
As natural disasters increase, the stakes around disaster waste management (DWM) are rising and planning becomes necessary. Yet, planning for DWM faces many obstacles, in particular regarding the lack of clear responsibilities. Who should be mandated to plan for DWM? What benefits and downsides does each potential planner offer? Is a centralised DWM planning process more effective than several? This article aims at answering these questions and assessing the assets and weaknesses of potential DWM planners, by looking into the case study of DWM after storm Alex in the Roya Valley (South France). Eight criteria can be considered to analyse the links between the stakeholders and their environment, and assess their relevance as DWM planners: geographic scale, time scale, resources, responsibilities, planning tools, coordination capacities, disaster, and waste. According to the existing literature, it seems that a comprehensive DWM plan is more detailed, centralises all the information and enables systematic waste treatments. However, in practice, the study shows that it is difficult to find an adequate stakeholder to develop such a plan and enhance the participation and collaboration of other stakeholders on this subject.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".