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Record W2947958498 · doi:10.5206/elip.v2i1.6205

International Students and the Canadian University

2019· article· en· W2947958498 on OpenAlexvenueaboutno aff
Stacey Zip

Bibliographic record

VenueEmerging Library & Information Perspectives · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsnot available
Fundersnot available
KeywordsOutreachInstitutionContext (archaeology)Public relationsInformation literacyPolitical scienceAcademic institutionHigher educationProcess (computing)SociologyPedagogyMedical educationLibrary scienceComputer scienceMedicineGeography

Abstract

fetched live from OpenAlex

This paper explores the dynamic international student populations within Canadian academic institutions and their relationship with the academic library. The international student body has evolving needs that must be adequately addressed by the library and institution if growing numbers are to continue being supported. Plagiarism, language barriers, and an unawareness of library services are well-known barriers to success, while more current issues such as changing technologies are equally problematic. Through such efforts as hiring an international student librarian, academic libraries may discover the specific issues facing their institution’s international students and begin the process of addressing them in a tailored manner. Data collection would be a priority for better understanding the international students, as would partnering with professors and student organizations to market library services and promote information literacy in a Canadian context. The international student librarian may also provide continued outreach to this demographic that is inclusive, proactive, and collaborative, which would in turn create an atmosphere that fosters international student success and is able to support rising numbers.

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)0.004
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.894
Threshold uncertainty score0.768

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.008
Science and technology studies0.0360.011
Scholarly communication0.0190.003
Open science0.0020.009
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0340.002

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 head0.008
GPT teacher head0.253
Teacher spread0.245 · 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

Citations4
Published2019
Admission routes2
Has abstractyes

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