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Record W4391234506 · doi:10.53103/cjess.v4i1.204

Faculty Perspectives on International Students' Educational Experiences in PPPs in Ontario

2024· article· en· W4391234506 on OpenAlexaffabout

Bibliographic record

VenueCanadian Journal of Educational and Social Studies · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic-Private Partnership Projects
Canadian institutionsGeorgian College
Fundersnot available
KeywordsMedical educationPsychologyMathematics educationPedagogySociologyMedicine

Abstract

fetched live from OpenAlex

The relevance of this study lies in the fact that public-private partnerships offer an effective platform not only for developing economic and socially significant projects, but also for finding effective mechanisms, such as increasing collaboration and a greater variety of joint programs, recognizing students' prior learning, and reducing barriers to student mobility. Surveying 300 part-time faculty members in multiple PPPs through a snowball survey method. This article compared faculty perceptions of value with the expected value of the Public-Private Partnerships (PPPs; P3) programs in Ontario catering to international students at satellite campuses. Through a critical examination of data, the author delves into comprehensive findings and offers recommendations to gain a deeper understanding of the matter from the faculty’s perspectives. It presents an overview of PPPs in education, student value from faculty perspectives, opportunities, and challenges of implementing partnerships. Moreover, it provides recommendations for future best practices tailored to ensure the success of private and public initiatives in the sector, throughout the collaboration between educational institutions and the government is essential to ensuring international students' well-being during their educational programs. A proposed well-being of International Students in Higher Education (WISHE) was further proposed.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.566
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.077
GPT teacher head0.356
Teacher spread0.279 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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