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Record W4290995302 · doi:10.26448/ae9789566095606.45

Migraciones de ayer y de hoy. Perspectivas y nuevos desafíos

2022· book· es· W4290995302 on OpenAlexaff
María Eugenia Cruset

Bibliographic record

VenueAriadna Ediciones eBooks · 2022
Typebook
Languagees
FieldSocial Sciences
TopicImmigration and Intercultural Education
Canadian institutionsNorthern Teacher Education Program
Fundersnot available
KeywordsHumanitiesPersonaPolitical scienceArt

Abstract

fetched live from OpenAlex

En la actualidad resulta innegable la interrelación de personas, países y culturas. Los avances en las comunicaciones, la mayor facilidad en el traslado de personas, mercaderías y capitales; pero también los avances institucionales supranacionales producto de la globalización han generado fenómenos antes poco vistos y que requieren nuevas herramientas para su análisis. Nuestra intención al nuclear los diferentes aportes, fue ofrecer un abanico de temas y problemas que no agotan la agenda cada vez más amplia y compleja de las problemáticas derivadas de los colectivos migratorios. Pero consideramos que la calidad y diversidad de los trabajos aquí presentados constituyen sin duda alguna un valioso aporte para la producción del conocimiento y el debate científico acerca de la construcción de la ciudadanía en un mundo global.

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)0.001
metaresearch head score (Gemma)0.001
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: Other · Consensus signal: Other
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.007
Scholarly communication0.0050.005
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.002

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.

Opus teacher head0.022
GPT teacher head0.321
Teacher spread0.299 · 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
GenreOther

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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