Defining and Analyzing Negative Policy Transfer: National Orphan Drug Regulatory Policy in Canada and Australia
Bibliographic record
Abstract
The existing policy transfer literature has largely ignored the study of negative transfer, resulting in various conceptualizations and a lack of tools for examining these cases. This study develops a novel analytical framework for explaining negative transfer, focusing on the three major pathways through which negative transfer emerges: constraints on the causal mechanism, constraints on the operationalization of lessons, and the application of negative lessons. It also develops a detailed definition of negative transfer, defining it as cases where the adoption of a specific policy or policy element is considered in the importing jurisdiction, but the transfer process results in the non-adoption of the policy under consideration. This definition addresses a gap in the policy studies literature, which has not yet expressly defined this process of negative policy transfer, thus providing a foundation for identifying cases and dynamics associated with negative transfer. An explicit definition also helps to distinguish between negative transfer and closely related concepts, including failed policy transfer and the full universe of non-adopted policies. This study then applies these tools to an underexplored area in political science: the transfer of national orphan drug regulatory policies in Canada and Australia. These cases present a puzzle for researchers, as Australia was an early adopter of a national regulatory policy in line with other jurisdictions internationally, while Canadian policymakers have rejected a similar policy several times since the late 1990s. Using a political development methodology and a mixed-methods design, this study finds that the difference in transfer outcomes is multifaceted and contextual, reflecting the importance of time and the shifting conditions in both cases. While Australia adopted a national policy, Canada did not due to various factors at different points in time, including conflicts of fiscal federalism and a lack of necessary norms of intergovernmental relations, political capacity, and consensus among experts on the necessity of a national regulatory policy. Path dependency and problem redefinition have impacted policymaking in both cases, but only contributed to negative transfer in Canada due to the timing of previously adopted policy instruments and the role of subnational governments in pharmaceutical pricing and coverage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".