Bibliographic record
Abstract
A lot has been written on Latin American cities, but they frequently remain an enigma to scholars seeking to decipher their past and present because of their rapid transformation in recent decades.This has prompted scholars to examine the accuracy of myths and representations of urban areas, and to ask whether the latter are intelligible to social scientists in the same way they are to novelists and poets.More specifi cally, recent studies have asked: How do the poor live in the metropolises of Latin America?Why do they often vote for conservative and populist leaders?What can public offi cials do about urban poverty?How can the spread of violence be halted to give a feeling of security in urban spaces?What marks do political periods leave on architecture, urbanism, and other cultural productions?How can one analyze the relations between political events and popular memory?How do social representations of cities infl uence political actors and researchers?The fi ve books reviewed in this essay respond to these questions in very diverse ways.Based on different data sources, methodologies, and contexts, each of these works presents an interesting perspective from which we can understand changes in Latin American cities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".