An Integrated Model of Legal Transplantation: The Diffusion of Intellectual Property Law in Developing Countries
Bibliographic record
Abstract
Why do some countries adopt exogenous rules into their domestic law when those rules contravene their specific interests? We draw on the policy-diffusion literature to identify four causal mechanisms that we hypothesize explain the adoption of such rules. While existing literature treats these mechanisms as independent, we argue that each works in combination with the others to facilitate legal transplantation. While one mechanism—coercion—tends to initiate the transplantation process, it fades over time and three others largely supplant it: contractualization, socialization, and regulatory competition. These mechanisms act in a mutually supportive manner. We test our claims via a quantitative analysis of legal transplants in the field of intellectual property (IP) that incorporates an original index of IP protection in 121 developing countries over more than 14 years. This article concludes with a plea for theoretical eclecticism, acknowledging multicausality and context-conditionality. Any comprehensive explanation of legal transplantation must include the identification of mutual reinforcement between causal mechanisms, rather than simply rank their relative contributions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".