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Record W2610808831 · doi:10.47678/cjhe.v47i1.186485

Graduand Student Attributes: A Canadian Case

2017· article· en· W2610808831 on OpenAlexaffvenueabout
Heather Kanuka, Summer Cowley

Bibliographic record

VenueCanadian Journal of Higher Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsConsistency (knowledge bases)Higher educationPsychologyStudent engagementUndergraduate studentMathematics educationQualitative researchProcess (computing)PedagogySociologyMedical educationPolitical scienceComputer scienceSocial science

Abstract

fetched live from OpenAlex

The purpose of this qualitative case study was to gain insights into how academics understand undergraduate graduand attributes. The findings reveal some alignment in views about student attributes, including that they are engaged citizens, are self-directed, have imagination, are questioning, are flexible, display leadership, are problem solvers, and possess character. This consistency, however, does not include the spectrum of views on how these attributes are conceived and developed. The findings reveal a range of interpretations regarding the kinds and levels of understandings of how graduand student attributes are developed throughout an undergraduate program of study. The findings indicate that (i) a shared understanding does not exist on how academics construe student attributes, (ii) academics do not share common meanings about the core achievements of a higher education, or how these are developed through students’ undergraduate programs, and (iii) student attributes tend not to be perceived as developing from the usual process of an undergraduate 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 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)0.004
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.948
Threshold uncertainty score0.380

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0290.007
Scholarly communication0.0040.001
Open science0.0020.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.072
GPT teacher head0.404
Teacher spread0.332 · 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

Citations6
Published2017
Admission routes3
Has abstractyes

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