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Record W2088133333 · doi:10.1177/0011392109104351

Encountering Globalization

2009· article· en· W2088133333 on OpenAlexaffabout
Judith Allsop, Ivy Lynn Bourgeault, Julia Evetts, Thomas Le Bianic, Kathryn Jones, Sirpa Wrede

Bibliographic record

VenueCurrent Sociology · 2009
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsGlobalizationState (computer science)Affect (linguistics)International marketPolitical scienceState policySociologySocial policyEconomicsLawInternational trade

Abstract

fetched live from OpenAlex

The market for professional services is increasingly international but comparisons have not been made between different professions nor on how state policies affect opportunities for mobility. This article considers three professions: engineers, physicians and psychologists and explores the similarities and differences in international labour market demand for occupations. It examines how state policies in four countries, Canada, Finland, France and the UK, aim to promote and control professional labour mobility and migration, and the differences across the three professions. Engineering is an international profession and the extent to which states encourage inward migration differs. Medicine is highly regulated in all four countries but inward migration of physicians varies depending on national policy. Psychologists are less mobile, and the extent of state sponsorship and regulation varies across countries. In all three professions, international organizations are a force encouraging global standards. The conclusion is that state policies reflect state interests and have a strong influence on patterns of mobility.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.010
Scholarly communication0.0080.008
Open science0.0010.008
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0170.001

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 head0.082
GPT teacher head0.510
Teacher spread0.428 · 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 designQualitative
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

Citations39
Published2009
Admission routes2
Has abstractyes

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