Collaboration between Corporations, Non-Governmental Organizations, and Indigenous Communities: The Bridge Strategy to Pursue Sustainable Development in the Oil & Gas Industry in Latin America
Bibliographic record
Abstract
Abstract Corporations investing in Latin America are facing problems including opposition from indigenous communities and environmental non-governmental organizations (ENGOs)1, and the activities of guerrillas and drug cartels2 that are making it difficult for them to operate. Collaborative alliances are seen as a strategy that organizations can use to deal with the turbulence and complexity of their environments3. Strategic bridging has proved to be effective to approach collaboration in sensitive areas of operation. The main conclusions are: (a) the complex environment in which corporations operates in Latin America requires collaboration among all stakeholders to achieve common goals of sustainable development; (b) indigenous communities have the potential to collaborate with MNCs; however, they need certain level of education and training; and (c) NGOs possess the necessary expertise to act as a bridge, their participation is essential to explore opportunities of collaboration between indigenous communities and MNCs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".