From Annihilation to Revitalization in Guatemala: Uses and Misuses of the Ixil Mayan Language in a Post-genocidal Justice Court
Bibliographic record
Abstract
Guatemala experienced a brutal internal armed conflict between 1960 and 1996. The conflict took on genocidal overtones in several autochthon regions, including the Maya Ixil area, where the author of this article conducted a year-long ethnographic field study as part of her doctoral thesis in social anthropology. The policies put in place as part of the counterinsurgency struggle undermined both the Ixil culture and its language, the use of which was banned in certain areas controlled by the army. Following the conflict, several associations sought justice for these various acts, and in 2013, a trial was opened against the country's former genocidal dictator. As part of this trial, witnesses were assisted by interpreters who simultaneously translated their accounts from Ixil into Spanish. Based on the testimony of a woman who was raped during the armed conflict, this author examines the linguistic negotiations that take place in a transitional justice tribunal and analyzes the effects of these negotiations on the memory of individual traumatic episodes and on their dissemination as narratives of a collective traumatic memory. Finally, by analyzing the different ways in which the experience of rape is expressed, this article sheds light on the changes in language that take place in the context of a trial, thus provoking broader questions about the way in which cultural differences are included in this type of space.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".