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Record W3135721476 · doi:10.4236/jsea.2021.142005

Investigation of the Academic Performance of College-to-University Transfer Students

2021· article· en· W3135721476 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Software Engineering and Applications · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsTrent University
FundersTrent University
KeywordsGraduation (instrument)Mathematics educationMedical educationSubject (documents)MatriculationPsychologyComputer scienceEngineeringMedicineLibrary scienceMechanical engineering

Abstract

fetched live from OpenAlex

Over the last decade, many universities/colleges have developed formal agreements which permit students from recognized college programs to be able to seamlessly transfer to a closely-related university program with advance standing. There has been some concerned raised that students that come to university from college may not be academically (or emotionally) prepared for the faster-paced university programs. This research, which was funded by an Ontario Council on Articulation and Transfer Faculty Fellowship, examines the academic performance of students in computer-related disciplines with a focus on comparing students who come to a university through a formalized college-to-university transfer agreement relative to students who enroll directly from high school. The comparisons will be based on metrics such as graduation rates, course failure rates, overall averages, course-level averages, and course-subject averages.

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.

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 score0.032
Threshold uncertainty score0.094

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.017
GPT teacher head0.294
Teacher spread0.277 · 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