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Record W4308731343 · doi:10.1177/00221856221131579

Through the back-door: How Australia and Canada use working holiday programs to fulfill demands for migrant work via cultural exchange

2022· article· en· W4308731343 on OpenAlexaffabout
Leah F. Vosko

Bibliographic record

VenueJournal of Industrial Relations · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsYork University
Fundersnot available
KeywordsImmigrationScholarshipWork (physics)TourismAccommodationSociologyPolitical scienceEconomic growthLawEconomicsEngineering

Abstract

fetched live from OpenAlex

In Australia and Canada, working holidaymaking is rationalized on the basis of encouraging cultural exchange among youth. Yet, in both countries, there is mounting evidence that working holiday programs are operating as back-door migrant work programs to help fill demands for labor in occupations and industries characterized by precarious jobs undesirable to locals. As scholarship on working holidaymakers’ labor market participation is more developed in Australia than in Canada, and administrative data available are also more extensive therein, this article sheds new light on the Canadian case vis-à-vis the Australian example. In exploring regulatory strategies adopted by these two settler states and their effects, comparative analysis of administrative data and historical and contemporary immigration and labor and employment laws and policies reveals how nationally specific program design can foster similar ends: precariousness among participants in the industries in which working holidaymakers are concentrated, including agriculture, tourism, and accommodation and food services. It also shows that stratification between working holidaymakers more closely approximating the image of the “cultural sojourner” and those who are effectively migrating for work purposes takes shape principally along the lines of source country in both countries.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.617
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.150
GPT teacher head0.324
Teacher spread0.175 · 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

Citations19
Published2022
Admission routes2
Has abstractyes

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