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Record W7015741787

Unintended Plagiarism Amongst International Students in Western Universities

2021· article· en· W7015741787 on OpenAlexaff

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsConcordia University
Fundersnot available
KeywordsNucleofectionHyporeflexiaPretextTSG101Articular cartilage damageDiafiltrationGestational periodLiquation
DOInot available

Abstract

fetched live from OpenAlex

In recent years, many western universities have experienced a notable increase of plagiarism and other behaviors that fall under the umbrella of academic dishonesty amongst students at various levels. Concurrently, higher-educational institutions have also seen a large rise in the enrollment of international students with extremely diverse cultural and linguistic backgrounds. A growing body of research has connected higher rates of plagiarism, in western universities during this time period, to international students in particular. This article reviews the current literature in this area, with a focus on the most common factors that put international students in western universities at a higher risk for unintended plagiarism than their counterpart domestic peers. The results showcase a complex myriad of factors that put international students at risk, such as new cultural, financial, or time stresses; underdeveloped language skills; the inability to self-advocate; and cultural differences in both schooling systems and viewing sources. Considering these factors, several practical implementations are recommended for institutions of higher education, with the aim of lessening international students’ risk factors of unintentional plagiarism, while simultaneously building academic confidence and community.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaResearch integrity
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptMetaresearchResearch integrity
Domain: Methods · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.285
Teacher spread0.264 · 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

Labeled directly by 2 models reading the full record.

Research integrityMetaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational
DomainMethods
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
Published2021
Admission routes1
Has abstractyes

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