The Perfect Data-Marriage
Bibliographic record
Abstract
There is a growing recognition in transitional justice research of the crucial significance of context-appropriate measures of justice practices and needs, which account for the diversity, locality, and complexity of individuals’ experiences of the past. In this perspective, this paper highlights the significance of oral history collections for exploring pluralistic understandings of the personal past and their relation to symbolic justice practices and needs. We argue that their audio-visual dimension and multi-layered nature makes them a unique qualitative data source that can contribute to a more realistic assessment of justice concerns in transitional settings. As tools of social dialogue and inclusive justice, they are also valuable means to promote the mutual acceptance and recognition of suffering and responsibility. We demonstrate how findings based on the analysis of survey data collected in Bosnia-Herzegovina (BiH) can be enriched by the exploration of oral history narratives from a dataset collected in BiH.
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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 | 0.055 | 0.300 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.177 | 0.088 |
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".