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Record W1179624981

Gender Differences in Engineering Education: An Exploratory Study

2010· dissertation· en· W1179624981 on OpenAlexaboutno aff
Ada Zacaj

Bibliographic record

VenueUWSpace (University of Waterloo) · 2010
Typedissertation
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsnot available
Fundersnot available
KeywordsEarningsPreferencePopulationExploratory researchPsychologySample (material)Gender gapSocial psychologyDemographic economicsSociologyDemographySocial science
DOInot available

Abstract

fetched live from OpenAlex

Despite significant efforts to boost female enrollment levels and retention rates in engineering programs, females continue to make up only a small portion of the Canadian undergraduate engineering student population. However, this traditionally-male field is undergoing a culture change as a result of the recent establishment of a female minority. New initiatives that are encouraging women to enter the field are also challenging assumed gender differences previously used to legitimize women's low participation. 
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\nThrough a series of multiple-choice, scenario-based questionnaires, this exploratory study seeks to establish whether or not gender differences observed in the broader population are applicable to the unique engineering undergraduate population at the University of Waterloo. In particular, respondents are quizzed on their preferences for specific job attributes and aspects of life outside of work. In addition, short-answer open-ended questions are used to gauge the level of integration experienced by female students in the faculty. Attention is paid to the general academic and social engineering environment as well as the specific dynamics of mixed-gender groups.
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\nAlthough some gender differences, such as higher preference for earnings on the part of males and work-life balance on the part of females, are in line with previous findings, other differences are found to be either absent or reversed. A surprising side effect of our culturally-diverse sample is the emergence of cultural background as a strong factor which, besides gender, affects work and life attribute preferences, especially preferences for task challenge and earnings. Another interesting outcome of the study is the resulting asymmetry between factors
\nthat respondents acknowledge as contributing to their happiness, and factors, which when absent, are found to contribute to the respondents' unhappiness. The study also reveals that female engineering students find themselves in a balancing act between perceived privileges due to their minority, and reduced participation and decision making power due to perceptions of engineering projects as stereotypically in the male domain.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score0.921

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.027
GPT teacher head0.235
Teacher spread0.209 · 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 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
Published2010
Admission routes1
Has abstractyes

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