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Record W4391602308 · doi:10.18260/1-2--44159

Work in Progress: A Trio-Ethnography on Professional Identity Development of Internationally-Trained Minoritized Women Early-Career Researchers in Canada

2024· article· en· W4391602308 on OpenAlexafffundabout
Anuli Ndubuisi, Glory Ovie, Zian Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicDoctoral Education Challenges and Solutions
Canadian institutionsThe King's UniversityUniversity of Toronto
FundersOffice of International Science and EngineeringSocial Sciences and Humanities Research Council of CanadaUniversity of TorontoAmerican Society for Engineering Education
KeywordsEthnographyIdentity (music)Career developmentWork (physics)SociologyGender studiesProfessional developmentPedagogyEngineeringAnthropologyArtAestheticsMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Work in Progress (WIP): The experiences of internationally trained minoritized academic researchers in engineering and education tend to deviate from the dominant developmental model of the doctoral program and faculty preparation. Our research extended the use of duoethnography methods to trio-ethnography and adapted Carlson and team's conceptual model of professional identity development [1] to investigate how internationally trained minoritized women early career researchers (ECR) build their professional identity construction throughout their doctoral study. Our preliminary findings highlighted three themes namely 1) Perception of Professional Identity, 2) Intersection of Race and Gender, and 3) Learning and Research Communities. The research findings provide approaches for mentoring international minoritized graduate students while improving developmental outcomes for historically racialized groups and other women in similar positions. Our study will contribute to the literature on the professional identity development of international minoritized learners in engineering and 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.657
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.313
GPT teacher head0.529
Teacher spread0.216 · 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.

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
Published2024
Admission routes3
Has abstractyes

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