Statutory and policy-based eco-disaster risk reduction in SADC member states
Bibliographic record
Abstract
Effective legislative framework is the cornerstone of managing hazards and disasters because they have become policy problems of global and local concern. This research study aims at understanding the implementation, strengths and gaps of policies related to Eco-DRR in SADC member states. In particular, attempts to critically analyse the making of DRM policies, as well as the variables underpinning these policies, given the high level of disaster losses. A literature survey was conducted to contextualise and conceptualise statutory and policy-based Eco-DRR. Academic literature on Eco-DRR and related policies, journal articles and related policies, official documents in SADC states including policies, acts, legislations, strategies, frameworks and plans were consulted. The analysis revealed that the Eco-DRR approaches have not yet been mainstreamed as part of standards of DRM in most of SADC member states, opting largely on ad hoc practice. Short-term plans and/or strategies don't help to articulate funding and programme priorities. In addition, irregular updating of policies in some member states and a lack of following up mechanisms were noted. Contribution: To change this reality, it is necessary to include Eco-DRR in strategies and/or plans and to standardise ecosystem-based measures for reducing disaster risks. Additionally, there is an urgent need for empowerment of the existing institutions and creation of networks that are driven by SADC institutions. Overall, it is evident that there is a regional interest and demand to apply and standardise ecosystem-based approaches and natural or green infrastructure solutions toward Eco-DRR.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".