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Record W4229800251 · doi:10.32920/ryerson.14662788.v1

Occupational mobility of Brazilian immigrants in segmented labour markets

2021· preprint· en· W4229800251 on OpenAlexaffabout
Giovanni Vendramin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMigration, Racism, and Human Rights
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsImmigrationInterviewFace (sociological concept)Occupational mobilityService (business)Order (exchange)Tertiary sector of the economyLabour economicsService providerBusinessDemographic economicsEconomicsPolitical scienceSociologyMarketing

Abstract

fetched live from OpenAlex

Canada needs immigration in order to maintain economic success, thus Canada accepts approximately 250,000 immigrants from countries around the world. Some of these immigrants find themselves gaining employment in the secondary labour market in the service and construction sectors. This paper aims to identify and analyze the experiences and issues Brazilian immigrants face in segmented labour markets. The study incorporates the knowledge and information gained from interviewing fifteen Brazilians who have recently immigrated to Canada and are employed in either the construction or service sector. After an in depth study, the following research will explore the issues revolving around occupational mobility, barriers to employment, educational credentials, and personal attitudes that Brazilian immigrants face in the labour 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 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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score0.638

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.003
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.325
Teacher spread0.303 · 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 designObservational
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
Published2021
Admission routes2
Has abstractyes

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