Investor Obligations in Special Economic Zones: Legal Status, Typology, and Functional Analysis
Bibliographic record
Abstract
ABSTRACT The article discusses various types of investor obligations in special economic zones and examines how they are utilized as instruments for devising development policies. It presents the evolution of regulatory models and practices related to investor obligations in the context of the unilateral character of the legal framework of the zones. The article distinguishes between two types of investor obligations. The first includes commitments focused on quantifiable aspects of economic performance of the investor in the host country, such as the maintenance of a pre-determined level of investment or the creation of a specific number of jobs. The second category of investor obligations is those containing qualitative goals that contribute to the host country’s developmental objectives, such as workforce welfare commitments, environmental standards, and technology transfers. Case studies of Shenzhen, Poland, and Tanzania are analysed to demonstrate how relevant regulatory practices have evolved over time. The case studies are drawn from three different phases of the global proliferation of special economic zones and reflect the regional diversity of the zones.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".