Epistemic competition in global governance: The case of pharmaceutical patents
Bibliographic record
Abstract
Abstract Expert consensus helps policymakers solve complex problems by identifying and legitimizing policy solutions. Yet, persistent hesitation remains among policymakers regarding the technically adequate policy solution despite the existence and mobilization of epistemic communities. This paper contends that more attention should be given to studying the epistemic competition that may arise when multiple epistemic communities grapple with the same problem but have divergent understandings of its technical nature and its adequate policy solutions. Building on Science and Technology Studies and on the literature on polarization, this paper suggests that two social dynamics, namely the mobilization of resources and increased polarization, may complexify the technical disagreement among experts. In turn, these dynamics may lead to a deadlock in the debates, negatively impacting the institutional context where they take place. To illustrate this, this paper analyzes the case of the pharmaceutical innovation system, which has been prone to tensions between experts arguing for strong patent protection and experts arguing for greater flexibility to meet public health needs. This paper builds on a mixed method combining a social network analysis of experts invited to provide their expertise in the WHO‐WTO‐WIPO Trilateral Cooperation events and on semi‐structured interviews with 24 of these experts.
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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 | 0.033 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.036 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.009 | 0.004 |
| 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".