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Record W4412710174 · doi:10.1016/j.ijdrr.2025.105732

Unpacking drought impacts and adaptive strategies in Morocco – perspectives from small-scale farmers

2025· article· en· W4412710174 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

VenueInternational Journal of Disaster Risk Reduction · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Resources and Sustainability
Canadian institutionsAgriculture and Agri-Food Canada
FundersEngineering and Physical Sciences Research CouncilUniversidade de São PauloGlobal Challenges Research FundUK Research and InnovationUniversité Mohammed VI PolytechniqueInstitut National de la Recherche AgronomiqueNewton Fund
KeywordsUnpackingScale (ratio)Environmental resource managementEnvironmental scienceGeographyCartography

Abstract

fetched live from OpenAlex

Droughts in North Africa are increasing in frequency and severity. The most vulnerable include small-scale farming communities and the associated small businesses with wide-ranging impacts and demands on local communities, food production, water allocation, and energy requirements. Our solution-orientated study supports the mitigation of impacts and the management of droughts in two case study areas within Morocco. A 2-day workshop was conducted to engage with local farmers in Settat and El Jadida to better understand the drought impacts and associated adaptive strategies. A Fuzzy Cognitive Mapping (FCM) approach was used to actively engage and interact with the farmers and to gain an understanding of the interconnections between drought, coping mechanisms, adaptation strategies, and issues to their livelihood and as seen through their experiences. The perspectives give new insights into where the policy interventions for the most impact in the system might be.

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)0.000
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 score0.137
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.246
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