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Record W2903644016 · doi:10.4324/9781315563954-7

Gender and Migration

2017· book-chapter· en· W2903644016 on OpenAlexaboutno aff
Susan Ressia, Glenda Strachan, Janis Bailey

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsBusiness

Abstract

fetched live from OpenAlex

This chapter highlights the difficulties experienced by female migrants in Australia within the broader international context. Skilled migration programs are now the major pathway for migrants entering traditional settler societies such as Australia, Canada, New Zealand and, more recently, the United Kingdom. Prior to migration, women felt their job search would be relatively easy and they would find employment quite quickly, and so would continue to develop their professional careers. The chapter explores the experience of migrant women with professional skills and experience in a new country, and the difficulties experienced by them as a result of gender, race and family responsibility. Gender segregation in new labour markets means that women fight for work within even more unfavourable labour market segments than men encounter. The gendered expectations of caring for the family, together with problems of skill and qualification recognition, mean that women are less likely than men to experience &s;economic betterment&s; as a result of the migration process.

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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0030.003
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0160.003

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 head0.045
GPT teacher head0.306
Teacher spread0.262 · 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 designTheoretical or conceptual
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

Citations4
Published2017
Admission routes1
Has abstractyes

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