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Record W6884659487 · doi:10.11575/prism/37304

A profile of post-secondary students in Alberta

2019· other· en· W6884659487 on OpenAlexaboutno aff

Bibliographic record

VenueOpen MIND · 2019
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCredentialMental healthService (business)Public healthRepresentation (politics)Attendance

Abstract

fetched live from OpenAlex

Many benefits are associated with obtaining a post-secondary education. This report used administrative data to profile students (18 to 25 years old) enrolled in publicly-funded post-secondary institutions in Alberta from the 2005/06 to 2010/11. Analyses examined these individuals’ sociodemographic characteristics and public service use patterns based on their enrollment status (full-time or part-time) and credential type. This report found that (1) almost one-third (120,000 to 130,000 a year from 2005/06 to 2010/11) of Albertan individuals 18 to 25 years old were enrolled in publicly-funded post-secondary institution, (2) there was a greater representation of female than male students, (3) about 3% of full and part-time students identified as Aboriginal, (4) part-time students were more likely to use mental health services than full-time students and non-students, (5) students were less likely to be high-cost health service users than individuals not enrolled in post-secondary studies, and (6) students enrolled in post-secondary studies, but not in credential programs, were more likely to use social services and income supports than other students. These findings provide policy-relevant evidence that public authorities may consider as they seek to better support post-secondary students.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.996
Threshold uncertainty score0.260

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.020
GPT teacher head0.328
Teacher spread0.309 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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