Defining negative cases of policy transfer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite calls to include negative cases in policy transfer research, little attention has been paid to the study of negative transfer cases. It is unclear whether scholars have answered these calls and what developments have been made in this area of policy transfer research. This article systematically reviews the literature on negative transfer cases to examine the extent to which negative cases have been included in transfer research, how negative cases have been defined, and what tools have been developed to study negative transfer cases. It finds that negative transfer cases have not been widely studied. In fact, several barriers exist which impede the study of these cases, including the lack of a common, explicit definition of negative transfer cases and comprehensive analytical tools for studying them. To facilitate future research, this article proposes a comprehensive definition of negative transfer cases. It also identifies several areas for further study to improve our understanding of policy transfer, including a greater focus on causal mechanisms and their role in shaping negative cases to develop a better understanding of the various pathways that result in negative transfer cases.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it