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Record W4321490879 · doi:10.5194/egusphere-egu23-2848

Analysis of the organization in place to manage flood resilience in Tahiti urban area (French Polynesia), as a framework for risk observatory

2023· preprint· en· W4321490879 on OpenAlexaff
Bastien Bourlier, Charlotte Heinzlef, Franck Taillandier, Corinne Curt, Damien Serre

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsFlood risk managementFlood mythGeographyEnvironmental planningContext (archaeology)Corporate governanceEnvironmental resource managementRisk managementPsychological resilienceVulnerability (computing)Psychological interventionBusinessPolitical science

Abstract

fetched live from OpenAlex

There are real needs to innovate in risk management approaches to address issues of vulnerable territories such as French overseas territories identified as particularly exposed to natural hazards. This communication focusses on the Tahiti urban environment in French Polynesia, a dense urban area, subject to coastal and river flooding hazards. French Polynesia is a semi-autonomous territory with a specific institutional context. The distribution of competences, the main development perspectives, or the management plans are specific to this territory. In addition, significant gaps and weaknesses in risk management have been identified in a governmental report in 2018 (isolation, lack of management plans). Our objective is to define the conditions for a resilient territorial organization for flood risk management, to highlight structure, issues and weaknesses.The method is based on a qualitative analysis of the current organization for the management of flood in Tahiti. For this purpose, we interviewed fifteen local actors in charge of flood risk management on the urban area (semi-directive interviews of about 45min). These actors belong to the different territorial levels (municipality, country and state). The aim was to collect information about their intervention capacity, the spatial inequalities of these interventions, but also the processes of communication and exchange between actors as well as questions inherent to competences share and local governance autonomy.The results highlight more precisely the gaps in risk management, better identify the specificities of the actions articulations, and finally to suggest ways of fostering the resilience of organizations. More precisely, they highlight the concentration of resources for crisis management phases, while other activities, such as prevention and urban planning, remain largely undeveloped. This research also emphasizes the adaptation capacities of the territory by solidarity processes and the existence of a significant risk culture. Furthermore, this study makes it possible to establish a framework, identify strengths and weaknesses as well as the role and methods of each stakeholder. Taking into consideration the bicephalous dimension of local governance (between French Polynesia and the French state) is a major issue implying the improvement of coordination and consultation processes.This approach provides a comprehensive view of the territory's organization to flood management and allows us to frame the operational conditions for the implementation of a resilience observatory helping long-term thinking and collaboration and, consequently, improving the effectiveness of the processes in place. This observatory will facilitate sharing and co-construction of data, cooperation, and also communication with decision makers.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.356

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.004
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.028
GPT teacher head0.306
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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