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Record W2014826105 · doi:10.2190/cs.16.3.c

The Path to Graduation: Factors Predicting On-Time Graduation Rates

2014· article· en· W2014826105 on OpenAlexaff
Jodi Letkiewicz, HanNa Lim, Stuart J. Heckman, Suzanne Bartholomae, Jonathan Fox, Catherine P. Montalto

Bibliographic record

VenueJournal of College Student Retention Research Theory & Practice · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsYork University
Fundersnot available
KeywordsGraduation (instrument)DebtLoanPsychologyMedical educationStudent debtHigher educationFinanceBusinessEconomicsMedicineEconomic growthEngineering

Abstract

fetched live from OpenAlex

This study uses an integrative persistence model to examine college students' expected time-to-degree as a function of sociological and economic factors. The data used in this study are from the 2010 Ohio Student Financial Wellness Survey (SFWS), a web-based survey of undergraduate college students. Of the students surveyed, 25% indicated that they plan to take longer than 4 years to complete their undergraduate degree. Findings from the study indicate college environment and personal financial characteristics are important factors in determining time-to-degree. Students who overspend, have a car loan, credit cards, or high debt, and those who feel stress from their finances are more likely to take longer than 4 years. Students are more likely to finish in 4 years or less if they live or work on campus, have a high GPA, or have met with a financial counselor or advisor. Implications for higher education administrators and parents are discussed.

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)0.014
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.066
GPT teacher head0.485
Teacher spread0.419 · 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

Citations56
Published2014
Admission routes1
Has abstractyes

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