The Role of Datasets in Transitional Justice Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In 2012, Brazilian President Dilma Roussef installed the Brazilian Truth Commission (CNV) to address gross human rights violations that occurred from 1946-1988. One of the most important sources of information available regarding this period is the files of the agencies that comprised the Brazilian intelligence system during the dictatorship. In total, there were around 12 million pages of relevant text in the National Archives. To make effective use of this trove of information, the CNV was challenged to use some data science tools to look for useful information within this huge dataset. As a result, a prototype of a data repository with selected documents (pdfs, images, etc.) has been created, which we summarize in this note. Computational tools for searching, organizing, and visualizing potentially important documents were developed and utilized to support CNV researchers. We also reflect upon the issues that complicated the CNV’s ability to gain access to reliable and comprehensive data and the limitations of analysis conducted with this type of research.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it