Bibliographic record
Abstract
Ciudad Juárez, across from El Paso, Texas, suffered an unprecedented downfall into violence and chaos between 2007 and 2012. It came to be known in 2010 as “the most dangerous city in the world.” What can cause a city to spiral downward into bloodshed and turmoil in the way that Ciudad Juárez did? This article makes the argument that the city's descent into violence and chaos is the result of a number of poor decisions made over the course of the 40 years preceding the bloodshed of the years under examination. The border in turn, this article argues, constitutes the most important contextual variable in determining the political, economic, social and cultural decision making of the city's leadership and its people. It was the city's overreliance on the advantages that its border location conferred on it for a long time what ended up generating a series of inbuilt weaknesses in its economic development model, its social and cultural fabric, and its political landscape that would eventually cause the city to collapse when external decision makers, from federal politicians to criminals, made decisions that exposed its inbuilt weaknesses.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".