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A complicated welcome: social workers navigate policy, organisational contexts and sociocultural dynamics following migration to Canada

2018· book-chapter· en· W4242490095 on OpenAlexaboutno aff
Marion Brown, Annie Pullen Sansfaçon, Stéphanie Éthier, Amy M. Fulton

Bibliographic record

VenuePolicy Press eBooks · 2018
Typebook-chapter
Languageen
FieldSocial Sciences
TopicResearch in Social Sciences
Canadian institutionsnot available
Fundersnot available
KeywordsSocial capitalContext (archaeology)Sociocultural evolutionPolitical sciencePoliticsPublic relationsGenerositySociologySocial scienceGeography

Abstract

fetched live from OpenAlex

Canada is promoted as a land of opportunity, with its natural beauty purportedly matched by the generosity of its people. Since 1994, Canada has been ranked in the top 10 places to live in the world, and in 2013 it placed third in the global ‘better life index’, recognised for its comfortable standard of living, low mortality rate, solid education and health systems, and low crime rate (Organisation for Economic Cooperation and Development [OECD], 2013). It is a promising option for migrant professionals looking to leave their home countries for a variety of reasons related to social, political and economic conditions. This chapter reports on the experiences of 44 social workers who undertook their social work education outside Canada and migrated to Canada with the intent of continuing to practise social work. We bring analysis to three key areas experienced as problematic: policy, including immigration, recognition of foreign credentials, and registration with the licensing body; organisational context, including issues related to the search for employment and process of hiring; and socio-cultural dynamics, the more subtle relations required to ‘fit in’ and feelings of ‘difference’ in relation to one’s colleagues. The findings for each of these are discussed in detail below, drawing on Bourdieu’s concept of cultural capital (Bourdieu, 1986).

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.903
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.053
GPT teacher head0.367
Teacher spread0.314 · 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
GenreOther

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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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