Cacophony of Voices: A K’iche’ Mayan Narrative of Remembrance and Forgetting
Bibliographic record
Abstract
This article analyzes the links and cleavages between collective and individual memory processes following a situation of war and terror. Nearly two decades after the most brutal period of Guatemala’s 36-year civil war, the notions of ‘memory’ and ‘truth’ have become critical socio-political issues; institutional memory projects have taken an important place in the peace process and in Mayanist political struggles. For many Mayan Indians, however, experiences of, and explanations for, past violence are not accommodated or represented by a unified narrative of ‘social memory.’ The postwar memory work of many Mayan Indians vacillates between a multitude of discourses and strategies (subjective, local, national and transnational) used to ‘make sense’ out of a chaotic past and unstable present. By examining the story of a single transnational K’iche’ Mayan man and placing it within its particular cultural, historical and communal context, the article problematizes the notion of ‘social memory’ and ‘truth’ as used conventionally in institutional political discourse. It also engages the scientific (psychiatric) literature on trauma and violence, which tends to frame memory within distinctly Western assumptions regarding individualism, morality and narrative coherence. It argues that, for Mayans whose memory work falls outside the boundaries of such authoritative institutions and discourses, the ability to balance a variety of world views and explanations for past brutality becomes a crucial coping mechanism in the fragmented post-war era, as it has historically.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.020 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".