Responsibility to Reconcile: Adopting New Terms to Foster Recognition
Bibliographic record
Abstract
The present article is the second of a three-series collection of articles that analyze the power of language to introduce a new set of concepts in contexts where societies can foster peacebuilding in post-conflict scenarios. I build up on Hannah Arendt’s moral responsibility work to understand the need to analyze responsibility in transitional justice frameworks while landing it on peacebuilding discussions. The article advances in how this concept can contribute to fostering recognition, the role it plays in [re]building civic trust and, ultimately, promoting reconciliation. This study is motivated to find a way to engage civil society in the process where social grounds are rethought on respect as a basis. I present a proposal to use the concept of responsibility while suggesting a definition circumscribed to the context of peacebuilding during transition processes.
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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 | 0.023 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.087 |
| Scholarly communication | 0.017 | 0.030 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.007 | 0.013 |
| 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".