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Record W7117651114 · doi:10.47678/cjhe.v55i4.190545

Navigating the Student Affairs Landscape: An Autoethnographic Exploration of the Student Affairs Profession from Canadian Perspectives

2025· article· en· W7117651114 on OpenAlexaffvenueabout
Cori Hanson, Heather A. Kelly, Sania Hameed, Shakeeb Ahmed, K. Li

Bibliographic record

VenueCanadian Journal of Higher Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsReflexivityStudent affairsWorkforceAutoethnographyExperiential learningNarrativeThematic analysisIntersectionalityHigher education

Abstract

fetched live from OpenAlex

This autoethnographic study examines the evolving professional landscape of student affairs in Canada through the lived experiences of five scholar-practitioners. Drawing on reflective narratives and thematic analysis, the study explores three central themes: unplanned career entry into student affairs, the expanding and complex demands placed on practitioners, and the dynamic construction of professional identity. Framed by the job demands-resources model and social ecological systems theory, this research reveals how intersecting personal, institutional, and systemic forces may shape the careers and commitments of student affairs professionals. Findings highlight tensions between credentialism and experiential knowledge, the emotional toll of equity work, and the precarity of institutional belonging. This article offers a nuanced understanding of the student affairs profession and calls for more inclusive, relational, and critically reflexive approaches to professional development, institutional policy, and workforce sustainability. It contributes to emerging discourses on practitioner well-being, professionalization, and systemic transformation in higher 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)0.001
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 score0.418
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.026
GPT teacher head0.389
Teacher spread0.362 · 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

Citations1
Published2025
Admission routes3
Has abstractyes

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