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
This paper examines the role of mixed and multi-level methods datasets used to inform evaluations of transitional justice mechanisms. The Colombia reparation program for victims of war is used to illustrate how a convergent design involving multiple datasets can be used to evaluate the effectiveness of a complex transitional justice mechanism. This was achieved through a unique combination of (1) macro-level analysis enabled by a global dataset of transitional justice mechanisms, in this case the reparations data gathered by the Transitional Justice Research Collaborative, (2) meso-level data gathered at the organizational level on the Unidad para las Victimas (Victims Unit), the organization in charge of implementing the reparations program and overseeing the domestic database of victims registered in the reparations program, and (3) micro-level population- based perception datasets on the Colombian reparations program collected in the Peacebuilding Data database. The methods used to define measures, access existing data, and assemble new datasets are discussed, as are some of the challenges faced by the inter-disciplinary team. The results illustrate how the use of global, domestic, and micro- level datasets together yields high quality data, with multiple perspectives permitting the use of innovative evaluation methods and the development of important findings and recommendations for transitional justice mechanisms.
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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 it