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Record W2751454458 · doi:10.1177/0019793916682448

Immigrant Language Fluency in the Low-Skilled Labor Market

2016· article· en· W2751454458 on OpenAlexaff
Ana Damas de Matos

Bibliographic record

VenueIndustrial and Labor Relations Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsFluencyImmigrationPortugueseWageSortingLabour economicsDemographic economicsEconomicsJob marketPsychologyPolitical scienceLinguistics

Abstract

fetched live from OpenAlex

Using longitudinal linked employer-employee data, the author investigates the returns to being a native speaker for immigrant men in the low-skilled labor market. She compares the two main recent immigrant groups in Portugal: Brazilians, who are Portuguese native speakers, and Eastern Europeans, who are not. Findings show that both wage level and wage growth of the two groups are similar. To better understand this surprising result, the author studies two mechanisms through which language fluency may lead to higher wages: sorting across occupations and across firms. Brazilians do sort into occupations that require greater language skills; however, this does not translate into a wage premium. Considerable workplace segregation occurs in Portugal, but Brazilians are not less segregated from natives than are Eastern Europeans. Evidence in this article suggests that language skills are not a major driver of economic assimilation in the low-skilled labor market.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.301
Teacher spread0.282 · 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.

Study designNot applicable
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

Citations3
Published2016
Admission routes1
Has abstractyes

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