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Record W2058447041 · doi:10.1080/07294360500453137

A profile of part‐time undergraduates in Australian universities

2006· article· en· W2058447041 on OpenAlexaboutno aff
Martin Hayden, Michael Long

Bibliographic record

VenueHigher Education Research & Development · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsFull-timeQuarter (Canadian coin)Government (linguistics)Sample (material)Part-time employmentMedical educationPsychologyDemographic economicsMedicineEconomicsGeographyEconomic growthEngineeringWork (physics)

Abstract

fetched live from OpenAlex

Over one‐quarter of all undergraduates attending Australian universities enrol on a part‐time basis. This paper addresses the social, educational and financial characteristics of these students. In a survey in 2000, questionnaires were sent to a sample of 84,591 domestic (i.e. excluding overseas fee‐paying) undergraduate students at 19 Australian universities. The response rate was 41.1%. Systematic differences were found between full‐time and part‐time respondents across a wide range of characteristics. Importantly, part‐time undergraduates were more likely to be older, in full‐time employment and concentrated in particular fields of study. Just over one‐half of all part‐time undergraduates would have preferred to be studying full‐time, financial circumstances permitting. Slightly less than one‐third felt prevented from studying full‐time because of a lack of government income support. A tenth of all part‐time undergraduates felt unable to study full‐time because of costs.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.211
GPT teacher head0.469
Teacher spread0.258 · 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

Citations18
Published2006
Admission routes1
Has abstractyes

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