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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.087 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.010 | 0.058 |
| Scholarly communication | 0.014 | 0.028 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".