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Record W4394503543 · doi:10.6084/m9.figshare.23259420

African migrations in transit through Costa Rica: reflections on the challenges of the institutional care

2023· dataset· en· W4394503543 on OpenAlexaboutno aff
Verónica Martínez Sánchez, Mara Coelho de Souza Lago

Bibliographic record

VenueFigshare · 2023
Typedataset
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsTransit (satellite)GeographyPolitical scienceRegional scienceEconomic geographyPublic transport

Abstract

fetched live from OpenAlex

Abstract. In recent decades there has been an increase of the amount of people from different countries from Africa who are transiting the American continent to reach the United States and Canada. Particularly in Costa Rica, since 2014 the presence of these migrations in transit has been more visible, which has produced different institutional responses to control and accompany their transit through the country. This article aims to make visible and problematize some of the main challenges present in the institutional attention to African migrations in transit in Costa Rica. Various interviews were conducted with officials in the area of migration (both from state institutions and international organizations) who have worked with this flow of migrants in the country.. Rather than going into depth on the public policies that permeate this migratory transit, the focus of the article is analyzing the concerns, tensions and contradictions present in the daily attention of this migration and also suggests practices that could strengthen more hospitable horizons with Africans in the country.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.307
Threshold uncertainty score0.611

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.006
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.135
GPT teacher head0.366
Teacher spread0.231 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

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