<scp>TRIPS</scp> and knowledge diffusion from low‐ and middle‐income countries
Bibliographic record
Abstract
Abstract Research Summary We examine a significant yet underappreciated effect of IPR implementation: the dissemination after TRIPS implementation of established scientific knowledge from low‐ and middle‐income countries (LMICs) into the global scientific system of pharmaceutical development. The staggered implementation of the policy allows identification of increased diffusion of pre‐existing LMIC knowledge on global diseases into the global corporate invention pipeline. For neglected diseases, the uptake remains in academic science. Other results demonstrate institutional effects in the scientific communities in LMICs through increases in scientific productivity, cross‐border collaborations, and scientist mobility. These and other results recast TRIPS’ impact as sensitive to the incentives of global corporations and institutionally significant for LMICs. We discuss implications for research on innovation strategy. Managerial Summary The implementation in an LMIC of an intellectual‐property system (e.g., of patents) carries implications for dissemination of pre‐existing scientific knowledge from the implementing country into the global scientific system. Corporate invention more intensively incorporates pre‐existing LMIC knowledge when the subject is global diseases such as cardiovascular conditions and cancer. However, when the subject is neglected diseases such as infectious conditions, the significant uptake remains in academic science. Overall, this research suggests that the implementation of patent and other intellectual‐property protections influences the integration of LMIC science into the global system differentially based on the relevance for global commercialization.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".