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Record W4408227835 · doi:10.1080/03075079.2025.2472913

‘I wasn’t my authentic self': identity concealment during placement-based work-integrated learning among students from equity-deserving groups

2025· article· en· W4408227835 on OpenAlexaff
David Drewery, Anne-Marie Fannon

Bibliographic record

VenueStudies in Higher Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsHigher educationEquity (law)PsychologyIdentity (music)Self-conceptPedagogySocial psychologySociologyMathematics educationPolitical science

Abstract

fetched live from OpenAlex

This study explores how students from equity-deserving groups (EDGs) manage concealable stigmatized identities during placement-based work-integrated learning (WIL). Through thematic analysis of interviews with 36 participants, we identify students’ motivations for identity concealment or disclosure, the strategies they use, the impacts on their WIL experiences, and their recommendations for institutional support. Students concealed identities to access work, avoid detection, and conform to organizational norms, while others disclosed identities to self-verify and embrace authenticity. Partial disclosure and full concealment emerged as key strategies, shaped by perceptions of workplace receptivity and the temporary nature of WIL experiences. The findings reveal that identity concealment impacts students’ social relationships and well-being in complicated ways and usually undermines the educative potential of WIL. Students called on institutions to address identity stigmatization by equipping employers with tools to support diversity, including improving job advertisement transparency, offering workplace cultural safety training, and facilitating meaningful student-employer interactions. By amplifying students’ voices, this study provides actionable insights for institutions to create culturally safe WIL environments.

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 score0.011
Threshold uncertainty score0.957

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 communication0.0000.000
Open science0.0010.000
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 head0.066
GPT teacher head0.439
Teacher spread0.373 · 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 routes1
Has abstractyes

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