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Record W2623422202 · doi:10.18260/1-2--2713

Attracting And Retaining Females In Engineering Programs: Using An Stse Approach

2020· article· en· W2623422202 on OpenAlexaffabout
Lisa Romkey

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMentorshipSocializationContext (archaeology)CurriculumEngineering educationEngineering ethicsPsychologySociologyPedagogyEngineeringSocial psychologyMedical educationMechanical engineering

Abstract

fetched live from OpenAlex

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Introduction There are great strides to be made in the recruitment of women to engineering programs and careers. While women typically make up more than 50% of individuals studying life science at most universities and colleges across North America, we see far fewer women in engineering programs than we do males. There is a considerable body of research that suggests ways to improve this, including mentorship programs, a change in the nature of the engineering workplace to accommodate family needs, and creating a more collaborative and less competitive atmosphere in both the academic and industry sides of engineering. Much of the literature on gender studies in science, technology and engineering suggests females enjoy and connect with these fields when they are placed within a human, social or environmental context. This paper demonstrates the why and how of this relationship, drawing ideas from gender roles and gender socialization. This paper looks at how moral development may impact a woman’s choice to pursue a career in the physical sciences, technology, engineering or math. In particular, the paper draws from Gilligan’s theories on females and the care-orientation of moral development, and how her theories demonstrate a need for a STSE (Science, Technology, Society and the Environment) orientation in high school, college and university curriculum. The extensive literature review in this paper is supplemented by qualitative data from 10 semi-structured interviews with female engineering students and recent female engineering graduates from a large engineering school in Canada. The subjects were interviewed individually, and came from a diverse set of academic and cultural backgrounds, engineering disciplines, interests and aspirations. The interviews were conducted in-person or via telephone, and were 30-45 minutes in duration. The interviews were structured around the following list of questions, however, the individuals interviewed were encouraged to share any thoughts on their experience, and some themes developed, and were encouraged, on an individual basis. • Why did you decide to pursue engineering? Do you feel that females have different reasons than males for pursuing engineering? • What were your first experiences with science and engineering as a youth? Which sciences were you most exposed to? • What were your most positive experiences in science and engineering prior to starting university? • Did you have any hesitation about pursuing engineering as a female? • What have been your most positive experiences, academically, as an engineering student? • What do you plan to do with your engineering degree? Do you think females have different goals than males? • Female numbers in engineering remain relatively low, and have recently been on the decline in Canada. Why do you think this is the case? How can we attract more women to the field of engineering? • Do you think there are stereotypes about engineering, or about women, that detract women from pursuing engineering? • Do you think the experience as a student is different for males and females? • If you could change something about your education as an engineer, what would it be?

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score0.210

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 head0.260
GPT teacher head0.312
Teacher spread0.052 · 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

Citations8
Published2020
Admission routes2
Has abstractyes

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