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Record W4412822101 · doi:10.1007/978-3-031-95151-0_1

Urban Solidarity and Mobilities in the Latin American Southern Cone: Local Experiences in Global Debates

2025· book-chapter· en· W4412822101 on OpenAlexfundno aff
Carolina Stefoni, Aline Bravo, Fernanda Stang

Bibliographic record

VenueIMISCOE research series · 2025
Typebook-chapter
Languageen
FieldSocial Sciences
TopicChina's Global Influence and Migration
Canadian institutionsnot available
FundersFondo de Financiamiento de Centros de Investigación en Áreas PrioritariasSocial Sciences and Humanities Research Council of CanadaAgencia Nacional de Investigación y Desarrollo
KeywordsSolidarityMobilitiesLatin AmericansCone (formal languages)Political scienceEconomic geographyGeographySociologySocial scienceLawComputer science

Abstract

fetched live from OpenAlex

Abstract In the American Southern Cone countries, solidarity cities have been developed mainly by the United Nations High Commissioner for Refugees (UNHCR) in collaboration with cities and local governments. Unlike in European countries, the United States, or Mexico, the idea of sanctuary cities has not been developed in this subregion. However, this situation does not necessarily mean that there are no initiatives by local governments and civil society to protect the human rights of migrants and refugees. The distance between the Northern and Southern approaches could be bridged by developing a different interpretation of the concept of sanctuary cities that we observe in the Southern Cone. This chapter identifies the elements that define sanctuary cities and explores whether the practices found in the Southern Cone are factually similar to those in the North. To address this issue, we conducted a documentary review involving the case studies of Brazil, Chile, and Argentina.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0090.012
Scholarly communication0.0050.003
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.048
GPT teacher head0.387
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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