Antecedents to social-ecological resilience in local humanitarian supply chains: evidence from African cataract camps
Bibliographic record
Abstract
Purpose Local humanitarian supply chains (HSCs) have experienced increasing social and ecological pressures over the past two decades. Enhancing their social-ecological resilience (SER) has thus become increasingly important. Surprisingly, the existing supply chain management literature does not provide unified theoretical explanations or practical guidelines for the SER construct. This study aims to fill this gap. Design/methodology/approach The authors investigate the antecedents of SER in local HSCs employing a qualitative empirical study of cataract camps in Africa, using semi-structured in-depth interviews with relevant experts and subsequent qualitative data analysis. Findings The findings highlight that while conventional resilience typically depends on the robustness and flexibility of associated HSCs, their SER is primarily determined by their actors’ engagement with the local conditions shaped by the regional communities, regulations, and environments. Originality/value The study offers a novel theoretical understanding and practical application of the SER construct in an HSC context, shedding light on regional challenges and opportunities. HSC managers and policymakers can build on them to shape the SER profile of their local HSCs.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".