Bibliographic record
Abstract
This article argues that the shortcomings of the currently dominant transitional justice model, which largely ignores considerations of social justice, can be explained by several structural factors within the international legal system. It considers the relatively recent establishment of transitional justice institutions and argues that despite different forms – international criminal tribunals, truth and reconciliation commissions, peoples’ tribunals – these institutions are motivated by similar rationales and have the same underlying objectives. These parallels, enhanced by the both explicit and implicit normative influences that the respective institutions have on each other, and the overly linear notion of time embedded in international law have given rise to a problematic model of transnational transitional justice. Among others, this model hinders the pursuit of social justice beyond a narrow focus on individual human rights and individualized responsibilities for specific crimes. The article calls for a deliberate turn away from prefabricated institutional responses as well as for a much-needed reconceptualization of the prevailing model of justice within international legal discourses in order to address structural inequalities and forms of injustice that are often part of the root causes of armed conflicts.
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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.095 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".