Innovation in business‐community partnerships: evaluating the impact of local enterprise and global investment models on poverty, bio‐diversity and development
Bibliographic record
Abstract
Purpose Over the last decade, several innovative business‐community partnerships have emerged to address simultaneously two pressing development issues: poverty reduction and biodiversity conservation. The purpose of this paper is to identify relevant models and to propose a first evaluation of these models in relation to development. Design/methodology/approach The models were identified following a literature review and were evaluated using Amartya Sen's definition of poverty. Findings The paper identifies two models: the local enterprise model and the global investment model. While the local model relies mainly on local resources, the global investment model includes local and global organizations and institutions. The paper has analyzed the respective impacts of these new business‐community partnerships, including their governance schemes, on communities and ecosystems through the lens of Amartya Sen's definition of poverty and development. The key finding is double. First, both these models are still in their very early stages. Second, the paper has identified the strengths and weaknesses of each of these models: while the global investment model provides access to solid and important financial resources and markets, the local enterprise model emphasizes local empowerment. Originality/value This paper reports innovative initiatives and models of governance that could inspire future private sector based approaches to biodiversity conservation and poverty reduction and help build the theoretical bases for such approaches.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".