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.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".