Foreign Aid, Domestic Institutions and Entrepreneurship: Fashioning Management Training Centres in China
Bibliographic record
Abstract
The literature on internationalization suggests several ways in which external forces can affect domestic institutional change. Explanations for the extent of the foreign impact include changes in international relative prices, capital flows into a country which create new organizations or restructure existing ones, external demand for structural adjustment, and transnational intellectual communities, which introduce universal norms into an otherwise non-conforming country. Yet domestic forces, such as political structures and institutions - including organizational ideologies, commitments to domestic constituencies, industrial structure or path dependence, local government entrepreneurship, and the local policy environment-all undermine the influence of external forces. This paper looks at the impact of overseas development assistance on three management training centres to assess whether foreign or domestic forces determined the rules, financial allocations, and pattern of organizational behaviour. It finds that domestic bureaucratic interests imposed powerful constraints on these new organizations. At the same time, foreign capital and global linkages helped these units evade some constraints that might have impeded their development. Despite China's image as a strong state, donors exerted significant influence over these projects. But each organization's property and internal rules, its domestic economic and bureaucratic environment, and the level of entrepreneurship of its leaders determined its pace and direction of development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".