Understanding Social Capital, Networks & Institutions: A Guide to Support Compost Entrepreneurship for Rural Development
Bibliographic record
Abstract
Compost micro-entrepreneurship has been used as strategy to increase the incomes of poor and rural farming communities. Nevertheless, several difficulties can arise to sustain these small businesses. The conversion of organic material into compost requires labor, tools and infrastructure. Many poor and rural microenterprises cannot afford all of these inputs to sustain operations. Literature suggests that social capital and collective action can address challenges related to limited resources for communities and small businesses. Little research, however, has explored how coworker characteristics and their cooperative efforts affect the financial sustainability of compost micro-enterprises. The objective of this study was to unveil whether rural compost microenterprises use social capital and/or collective action to address various challenges related to natural and financial capital, and if so, in what manner. A multisite case study framework was implemented using participant observation to identify common challenges faced by compost microenterprises in Chimaltenanago, Guatemala. Focus groups and semi-structured interviews were conducted to determine if coworker characteristics (related to social capital) addressed these challenges, and if so, how. Four characteristics related to social capital emerged from a thematic analysis, including 1) raw material access based on coworker occupation, 2) overhead savings from human capital, 3) credit/market-entry granted from social networks, and 4) consumer trust gained from social capital/gender. It appears the investigation and development of compost microenterprises should be more cognizant of opportunities related to coworker characteristics, especially those related to social capital and collective action. As a result, management training can be integrated within entrepreneurship development to sustain urban and rural economies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".