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Record W4391472959 · doi:10.1177/10704965241231550

Unpacking the Food Security Crisis in the Ecologically Fragile and Conflict-Ridden Lake Chad Basin: Interrogating NGOs' Response to the Climate Change-Security Nexus

2024· article· en· W4391472959 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Environment & Development · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsMcGill University
Fundersnot available
KeywordsNexus (standard)UnpackingFood securityClimate changeEnvironmental securityPolitical scienceFood insecurityNatural resource economicsGeographyEnvironmental resource managementDevelopment economicsEconomicsEcologyArchaeology

Abstract

fetched live from OpenAlex

A 2018 United Nations report highlights the growing need for funding and assistance to the Lake Chad Basin (LCB). The food security crisis in the LCB is a blend of complex factors relating to the declining water of Lake Chad and protracted insecurity fanned by Boko Haram insurgency. Unfortunately, development agencies sometimes focus less on how the climate change-insecurity nexus is becoming increasingly consequential in explaining the LCB’s profile in fragility. This paper considers the extent to which international agencies and nongovernmental organizations (INGOs) respond to multiple crises, integrating both climate change and security facets in their analysis and response to the food crisis besetting the LCB. Findings from interviews in Cameroon, Chad, and Niger reveal that NGOs fail to sufficiently take climate change into account in their policies and strategies, in that many food assistance programs are climate change neutral in content and focus.

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.

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)0.012
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 score0.869
Threshold uncertainty score0.870

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.271
Teacher spread0.240 · 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