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Record W2998337231 · doi:10.20431/2349-0381.0609017

Climate Change Impacts, Vulnerability, and Adaptation Options among the Lozi Speaking People in the Barotsefloodplain of Zambia

2019· article· en· W2998337231 on OpenAlexaff
Mwimanenwa Njungu, Sampa Moonga, Charles Namafe, Steriah Simooya, Inonge Milupi

Bibliographic record

VenueInternational Journal of Humanities Social Sciences and Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsVulnerability (computing)Adaptation (eye)Climate changeGeographyVulnerability assessmentEnvironmental planningEnvironmental resource managementSocioeconomicsPsychologyEnvironmental scienceSociologySocial psychologyComputer securityComputer scienceEcologyBiology

Abstract

fetched live from OpenAlex

The aims of this study were: -to find out how communities in the Barotse floodplain of Mongu district in Zambia are affected by climate change, establish adaptation opportunities practiced by the Lozi people and to raise awareness and stimulate interest in matters of climate change. Using primary and secondary data sources, it was observed that the negative impacts of climate change among the Lozi people include; increase in atmospheric pressure and excessive heat and flooding, prolonged spells of unexpected changes in seasons, reduction in food production and security, as well as inadequate clean water supply and extinction of some plant and animal species. The study also revealed vast local ecological knowledge that, if utilised, may help in the adaptation of climate change. The study further showed that climate change awareness and education are key in mitigating and adapting to climate change effects, though it was not found in the area. The study strongly recommend regular climate change awareness activities in order to promote mitigation and adaptation, need to pay greater and particular attention to the vast local ecological knowledge exhibited by the Lozi people that would help in adaptation to climate change in the area.

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)0.003
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.168
GPT teacher head0.381
Teacher spread0.213 · 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 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

Citations5
Published2019
Admission routes1
Has abstractyes

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Same venueInternational Journal of Humanities Social Sciences and EducationSame topicClimate Change, Adaptation, MigrationFrench-language works237,207