Standardization for Transnational Diffusion: The Case of Truth Commissions and Conditional Cash Transfers
Bibliographic record
Abstract
The study of the transnational transfer of practices and institutions generally looks at the intermediary and final stages of the process, with much less attention devoted to its initial steps. In contrast, this article theorizes the early part of the trajectory of transfer, conceptualized as the process through which local ideas and practices are turned into a “standard model,” which we term the process of standardization. Drawing upon the public policy and social movement literatures, we identify three potentially robust mechanisms as central to the process of standardization—certification, decontextualization, and framing—and apply this framework to two cases: the transnational spread of Truth and Reconciliation Commissions and the use of conditional cash transfers as a social policy instrument. We find that the key actors in shaping the content of these standards were neither the innovators nor the early adopters but intermediary entrepreneurs located at the intersection of a complex mix of state and nonstate networks.
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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 | 0.023 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".