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Record W1990731448 · doi:10.1017/s0008423906299979

Negotiating Citizenship: Migrant Women in Canada and the Global System

2006· article· en· W1990731448 on OpenAlexaboutno aff
Judith Soares

Bibliographic record

VenueCanadian Journal of Political Science · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsCitizenshipContext (archaeology)GlobalizationNegotiationPolitical scienceGlobal citizenshipState (computer science)CapitalismSociologyGender studiesPolitical economyLawPoliticsGeography

Abstract

fetched live from OpenAlex

Negotiating Citizenship: Migrant Women in Canada and the Global System , Daiva K. Stasiulis and Abigail B. Bakan, Toronto: University of Toronto Press, 2005, pp. 233. Negotiating Citizenship is a thoughtful, well-researched and insightful book concerned with the topical issue of citizenship and contesting citizenship rights within the context of global capitalism. It is a timely and comprehensive piece that interrogates and dissects traditional theories of citizenship, and offers an alternative theoretical perspective on the basis of examining the status and condition of foreign domestic workers and nurses who migrate to Canada from two similar but different regions of the world: the Caribbean and the Phillipines. In creating the context for discussion, the work takes the reader on the journey of these migrant women workers from their home countries in the “Third World” where national labour markets are becoming increasingly unable to absorb surplus labour and where they have been adversely affected by the anti-social structural adjustment policies of the International Monetary Fund (IMF), to the more economically developed Canada where the state encourages them to work without providing them with citizenship rights. By so doing, as the text points out, the Canadian state, which still has primacy under globalization, reinforces the vulnerability of an already vulnerable social group for which migration is a survival strategy.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.889
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.006
GPT teacher head0.228
Teacher spread0.222 · 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.

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

Citations28
Published2006
Admission routes1
Has abstractyes

Explore more

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