Communities and social enterprises in the age of globalization
Bibliographic record
Abstract
Purpose The goal of this paper is to develop a conceptual framework to understand the processes by which rural communities are using commons‐based social enterprises to engage global actors and forge local places. Design/methodology/approach The paper uses a four‐step conceptualization of commons‐based social enterprises within a complex world: deal with communities as complex systems embedded in larger complex systems, understand cross‐scale linkages between communities and other levels of organization, identify drivers of change, and build adaptive capacity to increase the resilience of communities in the face of globalization. The paper draws upon an international set of cases undertaken by the Centre for Community‐based Resource Management to illustrate each step. Findings Social enterprises are one means by which rural communities are negotiating with global actors through recent processes of globalization. The social enterprise provides a mechanism for rural people to secure tenure for common‐pool resources and allows them to make direct decisions regarding their management. Research limitations/implications To further develop the understanding of commons‐based social enterprises will require further integration of theory regarding commons and social enterprises. Practical implications States and development agencies lack enabling policies for commons‐based social enterprises that support the multiple goal strategies of rural communities for natural resources. Originality/value Commons and social enterprise literature have tended to exist in separate domains and this paper makes a first step toward their integration.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".