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Record W1624838056

Rural Versus Urban Students – Differences in Accessing and Financing PSE, Their PSE Outcomes and Their Use of Distance Education Research Projects

2005· preprint· en· W1624838056 on OpenAlexfundno aff
Laval Lavallée, Constantine Kapsalis, Alexander Usher

Bibliographic record

VenueMunich Personal RePEc Archive (Ludwig Maximilian University of Munich) · 2005
Typepreprint
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsnot available
FundersHuman Resources and Skills Development Canada
KeywordsGraduation (instrument)InstitutionDemographic economicsDistance educationRural areaSocioeconomicsEconomic growthBusinessPolitical sciencePsychologyEconomicsSociologyMathematics educationSocial science
DOInot available

Abstract

fetched live from OpenAlex

The most significant finding of the report lies in the relationship between distance from PSE institutions and PSE participation and type of institution chosen. Generally speaking, distance from PSE institutions or rural residency (the two are highly correlated) have important effects on the PSE decisions and outcomes of youth. These impacts vary inversely with income, that is to say, the lower the level of parental income, the greater the impact. Youth from rural communities beyond commuting distance to a PSE institution are less likely than youth from urban communities of comparable income levels to enrol in PSE; however, the gap increases significantly when rural families’ incomes fall below $40,000 per year. Moreover, regardless of income, they are more likely to enrol in a college if a university is not located within commuting distance (our analysis of YITS data also found substantially higher numbers of rural students in colleges than in universities). Distance does not appear to have a major effect on the choice of the field of study; however, there does seem to be some major differences between urban and rural students’ post-graduation incomes, at least among those who choose to borrow to finance their education.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0020.003
Research integrity0.0000.002
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 head0.110
GPT teacher head0.380
Teacher spread0.270 · 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

Citations2
Published2005
Admission routes1
Has abstractyes

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