No Amnesty from/for the International: The Production and Promotion of TRCs as an International Norm in Sierra Leone
Bibliographic record
Abstract
The literature on norms is dominated by debates over the definition of norms, discussion of the evolution of norms, norm diffusion, or norm implementation, and accounts of positive features associated with norms such as cooperation, mutual understanding. This paper argues that “the story” of international norms—or the dominant account of norms—is primarily a white, Western version that assumes that norms emerge from equal exchanges and relationship between states, denying the marked economic and political inequality between global actors and largely disregarding the intense contestations and controls associated with norms. In turn, this paper is an attempt to examine the tensions between so-called international norms and “local” norms and practices as well as the power dynamics and economic constraints that influence so-called global norms. Keeping in mind these tensions between the “story” of international norms and the practical constraints for local actors in the global south, this paper includes uses of the case study of Sierra Leone to examine what happens when “international” norms, such as those associated with transitional justice conflict with “local” norms. Specifically, the local norm of amnesty within Sierra Leone is studied in contrast to the international norm of truth and reconciliation commissions.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".