Organization and Society: Understanding Corporate Social Responsibility and The Inclusive Business in The Peruvian Business Environment
Bibliographic record
Abstract
The concepts and terminologies that link the efforts of companies and society for poverty reduction are increasingly numerous and lend themselves to confusion, both conceptually and in their practical business applications. Terms such as inclusive innovation, inclusive business, corporate social responsibility, sustainability, and third sector-social enterprise, all, in essence, have some link between the vision of a business and its contribution to society through its management model. But in practice, that linkage does not mean that they have the same approach and objective. The abundance of terms makes it difficult to create more dynamic progress toward the common goals established by the United Nations at the World Economic Forum in Davos (1999). These goals emphasize the relevance of collaborative contributions from the business sector for the preservation of social values and global economic progress, as well as sustainable global development. In this work, our objective is to clarify two concepts of business management—corporate social responsibility and inclusive business—in the Peruvian Business Environment. This research is intended to generate clarity for future research that seeks to strengthen the link between business and society in Peru. We present, as an introductory framework, a brief review of scenarios related to world poverty, and, poverty in Peru.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".