Towards the SDGs for gender equality and decent work: investigating major challenges faced by Brazilian women in STEM careers with international experience
Bibliographic record
Abstract
Abstract This paper aims to understand the main difficulties faced by women throughout their careers in Brazil and abroad. Based on the information gathered from these experiences, it seeks to advance the discussion on women's participation in STEM focusing on SDG 5 (gender equality) and SDG 8 (decent work). The main difficulties experienced by women in STEM as discussed in the academic literature were mapped. This provided input to develop a questionnaire containing qualitative and quantitative questions used to conduct interviews with women working in STEM. The sample consisted of highly qualified professionals working in high positions in the hierarchies of multinational companies in the STEM field with experience both in Brazil and abroad. The data collected was analyzed using a mixed-methods approach, including content analysis for qualitative questions and the Grey Relational Analysis for quantitative questions. The results revealed that the lack of flexible work systems, the scarcity of gender-sensitive organizational policies and labor policies, and the prevalence of traditional cultural models are some of the main difficulties faced both in Brazil and abroad by the women interviewed. The need to discuss issues of gender equality and decent work in the early stages of education is important for increasing women’s participation in STEM, which is a critical factor in the development of inclusive organizations and in fully achieving the sustainable development of society. This paper presents a unique perspective of the perceived difficulties faced by executive women who worked in Brazil and in different countries (i.e., Canada, Denmark, France, Germany, Switzerland and the United States). Gender equality in organizations is highly context-dependent, and cross-cultural analysis generates relevant insights to face the challenges and advance the discussion on women’s participation in STEM.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".