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Record W1535756253 · doi:10.14431/aw.2008.03.24.1.25

I Care for You, Who Cares for Me? Transitio nal Services of Filipino Live-in Caregivers in Canada

2008· article· en· W1535756253 on OpenAlexaboutno aff
Glenda Lynna Anne Tibe Bonifacio

Bibliographic record

VenueAsian Women · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
FundersUniversity of the PhilippinesInter-American Development BankJohn D. and Catherine T. MacArthur Foundation
KeywordsGerontologyPsychologyNursingSociologyMedicine

Abstract

fetched live from OpenAlex

Filipino women dominate the Live-in Caregiver Program in Canada since the 1990s. Although their entry is facilitated by a temporary work visa with stringent conditions under this program, there is an evident desire to move through the next immigration route as landed immigrants. The transition from temporary workers to permanent residents appears crucial especially in the lives of Filipino women who pave the way for the sponsorship, settlement, and integration of their families into Canadian society. Based on fieldwork in southern Alberta, this paper examines the settlement services provided to newcomers in Canada and their significance in the lives of migrant Filipino women caregivers during their transition from temporary to permanent residents. It outlines the sources of support and services utilized by these women in Canada as well as those provided by the Philippine government during this period. As caregivers, Filipino women exercise a fundamental social function to children, the elderly, and the physically challenged constituent members of Canadian society yet the corresponding programs and services responsive to their needs are not fully addressed by the host society. Female Filipino migrant workers mainly sustain the economic fate of its cash-trapped country as the acclaimed “new heroes” with no effective system of securing their rights and welfare overseas.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.007
GPT teacher head0.232
Teacher spread0.225 · 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 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

Citations10
Published2008
Admission routes1
Has abstractyes

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