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Record W1939856350 · doi:10.47678/cjhe.v30i1.183346

Studying Part-Time at University: From Research to Policy to Practice

2000· article· en· W1939856350 on OpenAlexaffvenueabout
David A. Keast

Bibliographic record

VenueCanadian Journal of Higher Education · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAttendanceSet (abstract data type)Order (exchange)Higher educationTime managementResearch policyPublic relationsPsychologyMedical educationPolitical scienceSociologyPedagogyMathematics educationManagementPublic administrationBusinessEconomicsMedicineComputer scienceLaw

Abstract

fetched live from OpenAlex

In the last few years universities in Canada have seen a noticeable decline in part-time enrollments. This trend has given rise to a number of pressing concerns regarding the needs and aspirations of part-time students and the status and future of part-time study. Unfortunately, these concerns exist against a backdrop of relatively little research on part- time university students, programs, and attendance which is useful for decisions on policy and practice. This paper highlights the results of research which examined part-time programming at Canadian universities and the needs and characteristics of undergraduate student populations with potential for part-time degree completion. It is argued that the mature, working, part-time learner constitutes a distinct nontraditional group, with a distinct set of educational needs and expectations. The findings suggest changes which may be necessary in university functioning in order to better serve these student populations in the future. Results are compared with other recent research and implications for institutional policy and practice 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)0.145
metaresearch head score (Gemma)0.252
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.256
Threshold uncertainty score0.767

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1450.252
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0050.012
Science and technology studies0.0130.033
Scholarly communication0.0310.017
Open science0.0090.014
Research integrity0.0180.015
Insufficient payload (model declined to judge)0.0160.001

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 head0.068
GPT teacher head0.448
Teacher spread0.381 · 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 designQualitative
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

Citations6
Published2000
Admission routes3
Has abstractyes

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Same venueCanadian Journal of Higher EducationSame topicHigher Education Research StudiesFrench-language works237,207