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Record W7132947126

Fostering Cross-border Learning and Engagement through Study Abroad Scholarships: Lessons from Brazil's Science without Borders Program

2015· dissertation· W7132947126 on OpenAlexafffundabout
Julieta Antonela Grieco

Bibliographic record

VenueTSpace · 2015
Typedissertation
Language
FieldSocial Sciences
TopicInternational Science and Diplomacy
Canadian institutionsCanadian Counselling and Psychotherapy Association
FundersDivision of Graduate EducationConsejo Nacional de Ciencia y TecnologíaCiência sem FronteirasConselho Nacional de Desenvolvimento Científico e TecnológicoCanadian Bureau for International Education
KeywordsScholarshipStudy abroadInternationalizationQualitative researchHigher educationHuman capitalInternational educationScholarship of Teaching and Learning
DOInot available

Abstract

fetched live from OpenAlex

This study examines the potential benefits of participating in the Science without Borders (Ciência sem Fronteiras - CsF) program, a study abroad scholarship program created in 2011 by the Brazilian government. Like other scholarship programs, CsF seeks to foster human capital development and the internationalization of science and technology in the country. Differently from other programs, however, CsF targets undergraduate students in the sciences. While Brazil has received positive feedback for this initiative, critics have argued that insufficient planning may hinder this program’s ability to deliver desired outcomes. Thus, through a literature review and 20 interviews with CsF scholarship recipients at the University of Toronto, this qualitative study evaluates the program’s ability to promote important benefits. Although the study identified various student benefits, the research also found structural issues that prevented all participants from benefitting equally, demonstrating the importance of collaborative planning and implementation of study abroad scholarship programs.

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)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0070.002
Scholarly communication0.0050.004
Open science0.0020.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.133
GPT teacher head0.643
Teacher spread0.510 · 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 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

Citations0
Published2015
Admission routes3
Has abstractyes

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