Constructing Memory amidst War: The Historical Memory Group of Colombia
Bibliographic record
Abstract
Between 2007 and 2013, we were part of the Historical Memory Group (GMH), a research group comprising researchers and experts working under the auspices of the National Commission for Reparation and Reconciliation of Colombia. The GMH was tasked under Law 975 with producing a report on the origins and causes of the armed conflict in Colombia. Despite the dominant right-wing political context and the ongoing armed conflict, the GMH enjoyed intellectual and operative autonomy in its research. This article interrogates the dynamics and reasons that served as the basis for the GMH’s special sensitivity towards victims; the notion of victim implicit in the research work, with its inclusions and exclusions; and the dilemmas that arose in the group’s work. We argue that the GMH can be characterized as an agent of knowledge production about a violent past that was able to articulate comprehensive and plural narratives about violence in Colombia. However, this work was limited by state and institutional dynamics that sought to domesticate and instrumentalize the voices of those who had been systematically silenced. A review of the GMH’s work suggests three critical dilemmas that constrain truth-telling mechanisms: the dilemma between opening spaces for truth telling and the safety of those providing testimony; the dilemma around whose victims’ voices gain authority in the documentation process; and the risks of institutionalizing a discourse around victims that bestows narrative capital to state and societal institutions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".