Bibliographic record
Abstract
In order to grasp the obstacles faced by peacebuilding processes it is important to analyze the basics: language. The way individuals are addressed matters because it determines their agency to narrate their stories, and freely explore their identities during the conflict’s aftermath. After studying Primo Levi’s “gray zone”, the relevance of studying the interstice between victims and victimizers becomes evident. The oxymoron makes visible a need to rethink the concepts in order to overcome the obstacle it represents during post-conflict’s reintegration processes. This article attempts to contribute to the unleashing of static concepts such as “victim” and “victimizer” while in periods of transition. These, under the premise that notions should be kinetic in accordance with the transitioning process they are part of. It is found that static concepts have a dissonance with labels anchored to a violent and complex past during peacebuilding efforts. Therefore, the article frames the transitional justice context by analyzing its objectives and special mechanisms, while revising what is understood by the words “victims” and “victimizers”. The ultimate goal is to problematize the findings and contrast them with the concept of violence. Mainly, because it is violence the one that creates, and mediates the relationship between both victims and victimizers. The article is part of a three-set of theoretical exercises. This one frames context to advance in the first steps to recognize and overcome the stigma imposed on conflict’s primary actors. It is the theoretical proposal to deepen on how to make reconciliation more attainable. Key words: Transitional justice, victims, victimizers/perpetrators, peacebuilding, violence, resentment.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.051 | 0.009 |
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".