Spatial associations between household and community livelihood capitals in rural territories: An example from the Mahanadi Delta, India
Bibliographic record
Abstract
Despite the increasing interest of the Sustainable Livelihood Framework in the field of international development and in academia and the recent call for the use of mixed-methods approach, there has been little analysis that brings together qualitative and quantitative methods over a large geographical extent. Based on findings from participatory rural appraisals during which participants identified the key assets needed to achieve their livelihoods, this paper argues that common-pool resources (community capitals) should be differentiated from private goods (household capitals) as they operate under different dynamics of decision-making and management. We then create quantitative indicators that can be mapped across a large geographical extent by using data derived from national census and satellite sensors. Spatial patterns and differentials in access to livelihood capitals across the case study are examined and the associations that exist between household capitals, between community capitals, and between both are quantified. The results demonstrate that household physical capital is positively associated with household financial and social capitals but negatively associated with household natural capital, supporting the hypothesis that households trade their natural assets to cope with shocks. It is also shown that proximity to main axes of communication increases access to village amenities but decreases access to natural resources, while remoteness increases household human capital but decreases household physical and financial capitals. Such a cross-scale study adds to the understanding of the question of scale regarding rural livelihoods and community development, which could act as a bridge between the implementation of policy programmes (often targeted at the community level) and their expected outcomes (often targeted at the household level).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".