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Record W4400482746 · doi:10.55016/ojs/cpai.v6i1.76920

“It Feels Weird Telling You For Sure”: Ambivalence and Uncertainty about Academic Integrity in International Students’ Self-Reports of Using Paid Academic Support Services

2023· article· en· W4400482746 on OpenAlexaffabout
Joel Heng Hartse

Bibliographic record

VenueCanadian Perspectives on Academic Integrity · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAmbivalenceAcademic integrityPsychologySocial psychology

Abstract

fetched live from OpenAlex

This presentation reports on a study that examines some international undergraduate students’ use of private academic support services (PASS), a growing phenomenon in Canadian higher education in the last decade whose practitioners often advertise to international and/or multilingual students. We situate the use of PASS at the intersection of three constructs: literacy brokering (Curry and Lillis, 2006), contract cheating (Lancaster & Clarke, 2016), and private supplementary tutoring/“shadow education” (Bray, 2008), seeking to understand why some students choose to pay for help with academic work and how they understand the ethics of their choices. Using a survey (n = 898 international student responses, with ⅔ being self-identified as users of English as an additional language) and semi-structured follow-up interviews (n= 23), this presentation addresses the following research questions: What is the type and nature of PASS that some students use, and why do they seek such services? How do students understand the use of PASS in relation to academic integrity? Survey results reveal 30% of participants described using some form of PASS, most commonly “homework help” websites or tutoring. Students described a variety of reasons for seeking PASS, often related to their perceived convenience and/or limited access to other forms of in-time support. Interviews revealed ambivalence about the use of PASS; while some reported feeling PASS were helpful in achieving academic goals, some described feeling uneasy or uncertain about their ethical acceptability or usefulness. We conclude with policy and pedagogical implications for this growing grey area of para-academic support.

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)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.346
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.006
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 head0.058
GPT teacher head0.434
Teacher spread0.376 · 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 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
Published2023
Admission routes2
Has abstractyes

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