So That All Shall Know/Para que todos lo sepan
Bibliographic record
Abstract
How does an artist respond to the horrors of war and the genocide of his or her people? Can art play a role in the fight for justice? These are key questions for understanding the work of Guatemalan photographer Daniel Hernández-Salazar. Since the 1980s, Hernández-Salazar has created both documentary and aesthetic works that confront the state-sponsored terrorism and mass killings of Guatemala's long civil war (1962-1996). His photographic polyptych (4-panel image) "Clarification" became the icon for the Recovery of Historical Memory project of the Archbishopric of Guatemala, as well as a rallying symbol for Guatemalans. Broadening his crusade for justice in the twenty-first century, Hernández-Salazar is now also using the shouting angel of his polyptych (entitled "So That All Shall Know") to challenge the forgetting and/or erasure of painful history in many parts of the world, including Mexico, Japan, the United States, Canada, and Argentina. So That All Shall Know is a powerful, comprehensive overview of the work of Daniel Hernández-Salazar on recent Guatemalan history. Portfolios of images present his early photojournalistic work documenting the Guatemalan genocide; his Eros + Thanatos series that responds aesthetically to the destruction of war; and his Street Angel project, which uses his image "So That All Shall Know" to protest against injustice and historical forgetting around the world. Accompanying the images are bilingual English-Spanish essays by four scholars who discuss the development of Hernández-Salazar's art in the context of contemporary photography, the social and political conditions that inspire his work, and the broader questions that arise when artists engage in social struggle. Introduced by Nobel Peace Laureate Rigoberta Menchú Tum, So That All Shall Know is a moving testament to the horrors of genocide and the power of art to give voice to the silenced and presence to the disappeared.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".