Does diversification enhance community resilience? A critical perspective
Bibliographic record
Abstract
Resilience has become a key component of how practitioners and scholars conceptualize sustainable communities. Given sustainability’s focal role in shaping international development funding, policies and programming it is imperative that we critically engage with the concepts embedded within the resilience discourse – including prescriptions for increased diversity. This article contributes to a discourse that questions this common recommendation for diversification, particularly as it relates to agricultural livelihoods and smallholder production. We provide examples from Ethiopia that demonstrate the two limitations of diversification. The first, that some forms of diversification are, in fact, maladaptive and reduce resilience. The second, that diversification is not always equal – some forms of diversification are only accessible to the most vulnerable. As the 2030 Agenda moves ahead in shaping what is considered important, and therefore funded and measured, we argue that much more context-specific nuance is required within the resilience discourse.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.068 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".