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Record W4412040420 · doi:10.37933/nipes/7.2.2025.7

Gendered Adaptation Strategies Among Sahelian Pastoralists Facing Climate and Pandemic Shocks in Northern Nigeria

2025· article· en· W4412040420 on OpenAlexfundno aff
O Olarenwaju E, J. Eromhonsele

Bibliographic record

VenueNIPES Journal of Science and Technology Research · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland Management and Livestock Ecology
Canadian institutionsnot available
FundersForeign, Commonwealth and Development OfficeScheme for Promotion of Academic and Research CollaborationInternational Development Research Centre
KeywordsPastoralismPandemicAdaptation (eye)GeographyCoronavirus disease 2019 (COVID-19)Climate change adaptationClimate changeEnvironmental planningSocioeconomicsLivestockEcologyBiologySociologyForestryMedicine

Abstract

fetched live from OpenAlex

Pastoralist communities in Nigeria's Sahel region are increasingly vulnerable to the compounded effects of climate change and public health crises such as the COVID-19 pandemic.This study explores gender-specific adaptation strategies in Bauchi and Gombe States, using a transformative mixed-methods approach involving 3,041 respondents.Quantitative surveys and qualitative interviews were conducted to assess the socioeconomic impacts of climate stressors and pandemicinduced disruptions on pastoral livelihoods.Findings reveal high levels of climate change awareness (>90%) but persistent livelihood losses, particularly in farming and livestock production.Statistical analysis using chi-square tests identified significant gender differences in coping mechanisms: women engaged more in premature harvesting and livelihood diversification, while men relied on sharecropping and outmigration.These results show a disproportionate adaptive burden on female pastoralists and underscore the need for gender-sensitive policy frameworks.The study recommends stronger investment in local climate adaptation, inclusive decision-making, and targeted support for women in pastoral systems.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.003
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 head0.029
GPT teacher head0.318
Teacher spread0.290 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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