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Record W3112604085 · doi:10.47678/cjhe.vi0.188817

Building Bridges to Better Bonds?: Differential On-Campus Participation between International and Domestic Students

2020· article· en· W3112604085 on OpenAlexaffvenueabout
Nicole Malette, Emily Ismailzai

Bibliographic record

VenueCanadian Journal of Higher Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInternationalizationBridging (networking)Ethnic groupLimitingPublic relationsHigher educationInternational educationSociologyPolitical scienceBusinessEngineering

Abstract

fetched live from OpenAlex

Helping international students create meaningful on-campus connections is a major part of higher education’s internationalization efforts. By focusing on the efforts made by both international and domestic students to develop a sense of belonging through on-campus organizations like clubs and sports, we have the opportunity to consider their active creation of bridging and bonding capital. Through structured interviews with 150 international Asian and domestic White and Asian students enrolled at one of the largest universities in Canada, this research demonstrates that ethnicity-based on-campus organizations play a key role in helping international students build bonding capital on campus. However, findings from this research also demonstrate that international and domestic student groups do not take part in the same on-campus organizations. Differences in participation and discriminatory attitudes held by domestic White students have the potential to inhibit bridging capital, limiting integration between student groups

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0050.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.027
GPT teacher head0.369
Teacher spread0.342 · 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 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

Citations11
Published2020
Admission routes3
Has abstractyes

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