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Record W3096021097

Understanding career persistence of women in engineering in Manitoba: a phenomenological study of former practicing members and graduates never registered

2019· dissertation· en· W3096021097 on OpenAlexaboutno aff
Kathryn Atamanchuk

Bibliographic record

VenueMspace (University of Manitoba) · 2019
Typedissertation
Languageen
FieldNeuroscience
TopicUndergraduate Neuroscience Education and Research
Canadian institutionsnot available
Fundersnot available
KeywordsPersistence (discontinuity)Interpretative phenomenological analysisPsychologyEngineeringSociologyQualitative researchSocial scienceGeotechnical engineering
DOInot available

Abstract

fetched live from OpenAlex

Women are underrepresented in the Canadian engineering profession at a rate of nearly 10:1 when compared to their male counterparts. This poses a problem for a profession whose goal is to provide innovative and inclusive solutions for all people. In Manitoba, while women are underrepresented in engineering at the same rate as the national average, there is also evidence that some either never enter the profession after graduation or leave at various stages in their career. This qualitative research study examined the experiences of two groups of women in Manitoba, Former Practicing Members and Graduates Never Registered, to understand the factors that both enable and deter their persistence in the engineering profession. Focus group sessions and one-on-one interviews provided an in-depth perspective of factors contributing to women’s career decisions. The findings generally align with the literature-based conceptual framework and indicate that this is a multi-dimensional problem that includes factors such as a need for improved work-life balance, workplace cultural shifts, and confidence building. Implications of these finding include a need to support both new graduates in the licensure process and former members who wish to return to practice.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score0.854

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0200.008
Scholarly communication0.0050.002
Open science0.0020.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.194
GPT teacher head0.284
Teacher spread0.090 · 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 designQualitative
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
Published2019
Admission routes1
Has abstractyes

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