Unpacking the Food Security Crisis in the Ecologically Fragile and Conflict-Ridden Lake Chad Basin: Interrogating NGOs' Response to the Climate Change-Security Nexus
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".