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
At age 43, Nayib Bukele, the President of El Salvador, who brands himself as a “philosopher king,” enjoys a 90 percent approval rating. His popularity is due to his transformation of a country that was once the murder capital of the world into a nation that is safer than Canada, according to his government’s data. Residents are free to walk in streets and lounge in parks that were former gang-controlled areas.By the end of August 2015, almost 4,000 people had already been murdered in El Salvador that year—on average one killing every hour. Largely fueled by warring gangs, by 2016, the country became the “murder capital of the world,” with a killing rate twenty-two times that of the United States. Violence had become normalized, schools were protected by barbed wire and patrolled by soldiers, armed private security guards stood at entrances to businesses, fear permeated daily life, shopping trips were circumscribed by safety concerns, and shopkeepers were commonly extorted by the gangs. Previous government “Iron Fist” crackdowns were deemed a failure at dismantling gang structures. However, Bukeley has taken the “Iron Fist” approach to a new level of human rights violations. The resulting calm on the street raises the question: at what cost has the apparent peace come.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it