Social and Political Risks: Factors Affecting FDI in China's Mining Sector
Bibliographic record
Abstract
Political risk can be defined as the potential for uncertainty and harm to business/economic operations that arise from political (governmental and other) behavior and events. These risks typically stem from factors such as economic structures, government institutions, policies, and societal characteristics, and are becoming more of a concern to prospective investors in a changing global political economy. This article seeks to expand upon the framework of political risk analysis by looking at “softer,” nonquantifiable risk factors. Through the analysis of foreign business experiences in China, we aim to demonstrate, via a qualitative case study of foreign direct investment (FDI) in the Chinese mining sector, that in addition to typical financial, operational, and geological factors, firms should be better aware of the particular sociopolitical and cultural risks that can harm their investments in a given industry. This study draws on primary fieldwork, focuses on micropolitical risks to the industry, and stresses that multinational corporations (MNCs) could be more cognizant of the many societal factors that can influence an investment success. © 2016 Wiley Periodicals, Inc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".