What's in an Idea?: Truth Commission Policy Transfer in Ghana and Canada
Bibliographic record
Abstract
This article employs a social constructivist framework to explain truth commission policy transfer across borders. Using the cases of Ghana and Canada, the article draws on qualitative interview research to trace how and why the truth commission model was adopted by these two countries in response to past human rights abuse. In contrast to suggestions that the transnational proliferation of truth commissions is the result of behavioral socialization emanating from “one-size-fits-all” international regulative structures, we argue that the idea of a truth commission is adopted and adapted by domestic agents, with the assistance of international actors, in the face of domestic constraints that bar the path to alternative transitional justice policy choices. Our findings show that actors both act upon and act within existing structures, and ideas play a significant, constitutive role in helping to shape peoples’ shared beliefs about the best way to address an abusive past. The article includes methodological explanation of what we can learn from nonparadigmatic transitions such as Ghana's and Canada's, and it concludes with a brief discussion of the role of international transitional justice actors.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.029 | 0.019 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".