MétaCan
Menu
Back to cohort
Record W3138207861 · doi:10.47678/cjhe.v27i2/3.183302

In Search of Credentials: Factors Affecting Young Adults' Participation in Postsecondary Education

2017· article· en· W3138207861 on OpenAlexaffvenueabout
E. Dianne Looker

Bibliographic record

VenueCanadian Journal of Higher Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsAcadia University
Fundersnot available
KeywordsAffect (linguistics)Postsecondary educationHigher educationEducational attainmentHabitusPsychologyNova scotiaDemographic economicsSociologyEconomic growthCultural capitalEconomicsSocial science

Abstract

fetched live from OpenAlex

This paper uses longitudinal data from a survey of youth in three areas (Hamilton, Halifax and rural Nova Scotia) to examine the factors that affect young adults' participation in postsecondary education, applying Bourdieu's notions of capital and habitus. Data were collected from 1,200 youth in 1989, with questionnaire follow-ups in 1992 and 1994. The analyses examine (a) the factors the youth themselves say affect their educational decisions and (b) cross-tabulation and regression results that document the variables empirically related to the youth's educational expectations when they are seventeen and their attainments by age twenty-four. Cost factors were found to be a major deterrent as were, for some youth, their knowledge of and attitudes to schooling. Parental education and income affect their children's decisions. University is seen to be "the" preferred postsecondary path; other institutions such as community colleges seem to be the "fall back" option for those who cannot or do not get to university. Results are relevant to an understanding of the persistent impact of parental capital and of one's attitudes on educational outcomes. There are also policy implications regarding the resources needed by different students to better access the postsecondary options available to them.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.121
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.437
Teacher spread0.397 · 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.

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
Published2017
Admission routes3
Has abstractyes

Explore more

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