Corporate Social Responsibility: The Business Case RISK ANALYSIS AND CONFLICT IMPACT ASSESSMENT TOOLS FOR MULTINATIONAL CORPORATIONS
Bibliographic record
Abstract
This is the first of several reports that identify a role for CIFP in providing a risk assessment service to the private sector. In discussing the role of the private sector in preventing violent conflict, this report focuses primarily on multinational corporations (MNCs). Issues related to the risks and responsibilities of MNCs in conflict-prone regions are distinct from those concerning other types of private sector actors. Large companies involved in foreign direct investment in the extractive, infrastructure and heavy industry sectors are of particular interest, due to the heightened potential for their activities to exacerbate conflict. In addition, MNCs are likely more able to implement conflict prevention mainstreaming strategies than smaller domestic enterprises for a variety of reasons. Therefore, the following analysis relates specifically to the risk assessment needs of MNCs. For a definition of important concepts and terms, please refer to the glossary. About the author Tricia Goulbourne is a candidate in the International Affairs programme at Carleton University. Tricia specializes in international finance and trade. About CIFP CIFP has its origins in a prototype geopolitical database developed by the Canadian Department of National Defence in 1991. The prototype project called GEOPOL covered a wide range of political, economic, social, military, and
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".