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Record W2140803546 · doi:10.1177/0020872806066765

Opportunities and challenges for social workers crossing borders

2006· article· fr· W2140803546 on OpenAlexaboutno aff
Ruth C. White

Bibliographic record

VenueInternational Social Work · 2006
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsnot available
FundersU.S. Department of StateU.S. Department of Homeland Security
KeywordsHumanitiesSociologySocial workPolitical scienceArt

Abstract

fetched live from OpenAlex

English Using the author's experience as a social worker who has studied and worked in Canada, Belize, Brussels, Jamaica, London and the USA, this article explores some of the issues facing social work students, scholars and practitioners who migrate across international borders. Topics include equivalency of qualifications, visas and socio-cultural issues. French L'auteur de cette recherche s'appuie sur son expérience d'étudiant et de professionnel dans le domaine du travail social au Canada, au Belize, à Bruxelles, en Jamaïque et aux États-Unis pour explorer certains enjeux auxquels sont confrontés les étudiants, les boursiers ou les praticiens oeuvrant à l'international. Les équivalences en matière de qualification, les visas et les questions d'ordre socioculturelles sont au nombre des thèmes abordés. Spanish Basándose en la experiencia del autor, que como trabajador social estudió y trabajó en el Canadá, Belice, Bruselas, Jamaica, Londres, y los Estados Unidos, se exploran algunas cuestiones con las que se encuentran estudiantes, investigadores y profesionales de trabajo social cuando emigran fuera de sus países. Se exploran los temas de equivalencia de títulos, visas y asuntos culturales.

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)0.007
metaresearch head score (Gemma)0.009
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.175

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0370.020
Scholarly communication0.0150.009
Open science0.0020.021
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0120.001

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 head0.156
GPT teacher head0.407
Teacher spread0.251 · 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

Citations34
Published2006
Admission routes1
Has abstractyes

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