MétaCan
Menu
Back to cohort
Record W2883704194 · doi:10.1080/03057925.2018.1479185

Insights gained from a comparison of South African and Canadian first-generation students: the impact of resilience and resourcefulness on higher education success

2018· article· en· W2883704194 on OpenAlexaffabout
Maureen J. Reed, Mandivavarira Maodzwa – Taruvinga, Elizabeth S. Ndofirepi, Raazia Moosa

Bibliographic record

VenueCompare A Journal of Comparative and International Education · 2018
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDisadvantagedPsychological resilienceResilience (materials science)PsychologyDevelopmental psychologyFirst generationSocial psychologyPolitical scienceSociologyDemography

Abstract

fetched live from OpenAlex

First-generation students are often described as disadvantaged in university adjustment, self-efficacy and grades. Yet this deficit model of understanding first-generation students ignores their cultural capital, which could increase resilience and resourcefulness. Here, 844 students (31% first-generation) in South Africa and Canada completed measures of resilience, resourcefulness, university adjustment, academic self-efficacy and self-reported grades. Overall, the results reveal that the characterisation of first-generation students is culturally specific and, in some ways, differs between Canada and South Africa. That is, the deficit model may better describe Canadian than South African first-generation students. Yet, in many ways first-generation students are like their peers and their academic outcomes are predicted by their culturally specific levels of resourcefulness and resilience. This study support the notion that the positives students bring to university should be considered and that students would benefit from being taught the requisite skills involved in increasing resourcefulness and resilience.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.145
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.097
GPT teacher head0.480
Teacher spread0.382 · 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

Citations16
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueCompare A Journal of Comparative and International EducationSame topicResilience and Mental HealthFrench-language works237,207