Ways of Knowing Atrocity: A Methodological Enquiry into the Formulation, Implementation and Assessment of Transitional Justice
Bibliographic record
Abstract
This Special Issue of the Canadian Journal of Law and Society proposes that clashes over the different ways of knowing atrocity are central to understanding the contours of the debates and disagreements over transitional justice. Bringing together scholars and practitioners in politics, law, literature, statistics, anthropology, history, and development studies, the special issue focuses on the processes used to respond to atrocity starting with how we know about the nature of harm, and following this, what methods are used to both respond to these abuses and evaluate these responses. In doing so, the collection maps the forms through which knowledge on atrocity is conveyed and simultaneously explores how the form influences its content.An increased sensitivity to the forms through which different actors know about atrocity draws attention to two over-arching themes developed across all seven of the articles included in this special issue. First, transitional justice processes provide a means of categorizing abuses. In doing so, they set the parameters of what type of harm warrants a response. Acknowledging the types of classification and the sources that underpin them sheds light on both what is made visible and what is rendered invisible in our current response to serious human rights violations. Second, when read together, the papers draw valuable attention to the researcher as a relational agent producing knowledge on atrocity, not only through determining the choice of method and the area of enquiry but through building sets of relationships that are a part of the response to the abuse. Acknowledging the relational aspect of both the practice and the research of transitional justice highlights the ethical obligations associated with obtaining access and claiming expertise when responding to serious human rights violation.
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 | 0.072 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.030 | 0.090 |
| Scholarly communication | 0.038 | 0.028 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".