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Record W3153562331 · doi:10.5539/jsd.v14n3p95

Assessing Community Adaptation Strategies to Floods in Flood-Prone Areas of Urban District, Zanzibar, Tanzania

2021· article· en· W3153562331 on OpenAlexvenueno aff
Badriya S. Nassor, Makame Omar Makame

Bibliographic record

VenueJournal of Sustainable Development · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsTanzaniaFlood mythFlooding (psychology)GeographyEnvironmental planningInterviewAdaptation (eye)SocioeconomicsEnvironmental resource managementEnvironmental protectionEnvironmental sciencePolitical scienceArchaeologySociology

Abstract

fetched live from OpenAlex

Floods disasters around the world have increased for the last 20 years and affected billions of people. The same has been observed in Zanzibar, which resulted in severe impacts in many parts of the urban-west region and affected many people, threaten several lives and caused substantial economic losses. Therefore, this study intended to assess the community adaptation strategies to floods, the genesis of those strategies and the limiting factors for each adaptation strategies in flood-prone areas in the Urban District in Zanzibar, Tanzania. It involved 399 households. Data were collected using an interviewer-administered questionnaire for heads of the households to assess their adaptation strategies. The study discovered that the community has been employing different adaptation strategies to reduce the floods risk at pre, during and after floods. Before flooding is cemented the floor, while during flooding moved to another place and after flooding did the structural repairs of their houses and recommendations to the government on providing necessary support are delineated.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.266
Teacher spread0.247 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations11
Published2021
Admission routes1
Has abstractyes

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