A Review of Influencing Factors for Selection of Engineering Pathway for Women – A Case Study of Females Studying Engineering at Waikato Institute of Technology (Wintec), New Zealand
Bibliographic record
Abstract
Females are underrepresented in engineering cohorts in New Zealand. The lack of female participation in engineering fields at the tertiary education level has been a barrier for diversity and equality in both the industry and academic professions. A recent study by Docherty et al. [11] noted girls coming to engineering at Canterbury University, New Zealand are more likely to be from a single sex school and this phenomenon can be due to cultural reasons. They identified that future work is needed to look at the cultural changes in New Zealand which could potentially mitigate the gender bias.However, we first need to identify a range of contributing factors (including cultural issues) for the lack of diversity in engineering schools in New Zealand. By identifying these factors, we can then propose and implement necessary remediation actions to address the lack of female participation in engineering. Common influencing factors for female participation in STEM and selection of engineering pathways were found during a review of literature and included parental and teacher influences, self-efficacy, perception and attitude, gender stereotypes, and peer and media influences. We believe that New Zealand context in terms of how it influences female study and career pathway to engineering has not been well studied and documented to date. The objective of this research is to identify the main factors and cultural issues that contribute to low female participation in engineering studies in New Zealand. We carried out individual and focus group interviews on both domestic and international female students at Wintec enrolled in the Diploma, Bachelor of Engineering Technology and Graduate Diploma programmes in Civil Engineering. The interviews helped us to understand our students’ perspectives around the factors that influenced their study decisions. We used the collected data to identify patterns and generate themes. n the New Zealand context, we found, barriers to selection of engineering pathway for females include the school system; lack of career and subject choice guidance available to students at school, lack of promotion of the profession, and society’s perception of engineers as being masculine - “a tradie working in a workshop”. For our international students’ participants, it appears that the school system in their country directed them (regardless of gender) to maths and engineering study pathways if they showed talent in these areas and engineering is a highly regarded profession.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".