Bibliographic record
Abstract
Business plays a central role in international development as both an intentional and unintentional actor. This paper evaluates the role of business as an international development actor and considers the benefit corporation, a for-profit entity that holds in equal part public benefit and profit within their mandate, as a potential avenue for businesses to play an intentional positive role. The current role of business in international development is hard to define, but its effects are certainly mixed. What is clear is that the behaviours of businesses have significant impacts on both human and environmental security. Many development efforts are based on the belief that a strong private sector and competitive markets are essential conditions for development. This has defined business’s role in development as mostly geared towards wealth creation, employment, and providing goods and services. Business practices and their effects on communities globally have repeatedly demonstrated the need for a code of ethics and the importance of caution and impact assessments as businesses shift into intentional roles as development actors. The benefit corporation model provides an opportunity for businesses to operate internationally while playing a positive role in international development. This paper uses CSR theory, a framework of classification for development agents, and a case study of the benefit corporation Patagonia to evaluate the viability of the benefit corporations as international development actors.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".