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Peruvian Lives across Borders

2018· book· en· W4240191423 on OpenAlexaboutno aff
M. Cristina Alcalde

Bibliographic record

VenueUniversity of Illinois Press eBooks · 2018
Typebook
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsAmbivalenceScholarshipNegotiationImpossibilityMiddle classGender studiesEveryday lifeIdentity (music)SociologyClass (philosophy)GeographyPolitical scienceSocial scienceSocial psychologyArtPsychologyAesthetics

Abstract

fetched live from OpenAlex

Peruvian Lives across Borders focuses on the transnational lives of middle and upper-class transnational Peruvians. Among the Peruvians whose migration trajectories this book examines, return as a possibility, impossibility, or reality looms large. The lens of return provides one way to understand what transnational Peruvians desire, reject, or feel ambivalent about in constructions of home and Peruvianness. Employing return as a critical lens and through an intersectional approach, the book presents an intentional departure from the more prevalent focus on international labor migrants from lower and working classes in migration scholarship, and particularly among anthropologists. It suggests that a critical examination of middle and upper-class Peruvians’ migration experiences reveals as much about individual trajectories and class dimensions of migration as about broader constructions of Peruvianness and home that inform the everyday lives of Peruvians across multiple differences and spaces. A close look at Peruvian individual lives across settings in the United States, Canada, Germany, and Peru, and affective and material attachments to and practices in those settings, exposes the lived realities of everyday negotiations surrounding return to a home that is fundamentally made up of processes of inclusion and exclusion based on social hierarchies of gender, location, language, race, sexual identity, and class.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.730
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.269
Teacher spread0.248 · 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 teacher head, 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".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

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