Bibliographic record
Abstract
This article investigates the deployment of dependency as a keyword in discussions of food security in South Sudan, on the basis of interviews and observations carried out in December 2012. Our initial intent was to estimate challenges to rural food security as the country emerged from decades of violent conflict. However, the notion of a “culture of dependency” arose persistently from our data, alongside more conventional information about food. We contextualize this discursive deployment of dependency within ongoing scholarly debates about the existence of “dependency syndromes” in humanitarian relief operations in central Africa and within academic discussions of the power of buzzwords and keywords in development discourse, with particular reference to Swidler and Watkins's 2009 article “‘Teach a Man to Fish’: The Doctrine of Sustainability and Its Effects on Three Strata of Malawian Society.” We argue that dependency in the South Sudanese context incorporates four facets: the near-total economic dependency of South Sudan on oil revenues; the social-structural dependency of rural communities on international nongovernmental organizations (INGOs) for basic foodstuffs; a so-called “culture of dependency” that our informants claimed had taken root in rural areas, so that local people had lost old habits of autonomy and self-reliance; and the reliance of INGOs on the populations they serve. We do not empirically validate these “dependencies” but treat them as discursive constructs with potentially major implications for rural development.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".