Overcoming Gender Stereotypes in the Process of Social Development and Getting Higher Education in Digital Environment
Bibliographic record
Abstract
Social development and higher education are among the essential tools for overcoming gender stereotypes. The changes in education associated with the digitalization of learning and work, studies show, have little changed the gender landscape. Studying the opinion of students on the problem of gender relations and stereotypes is relevant in terms of the need to determine the impact of higher education in digital environment on changes in perceptions of behavioural patterns and social roles of men and women. This study examines the impact of gender stereotypes, manifested even in online learning and communication and when working in the new digital economy on student’s choice of a life path, profession, education. The research aims to study the influence of students’ social development in the learning process on the formation of gender stereotypes among them. An anonymous written survey was the most suitable method of the study. The survey involved 350 students of socio-humanitarian, technical and natural specialities (60% − women, 40% − men). It was found that getting higher education in digital environment is crucial to social development, as this stage of life helps individuals overcome gender stereotypes. Yet, gender stereotypes continue to dominate among students anyway. To which extent do students agree that the primary purpose of a woman is the role of wife and mother? Most students (44%) agreed with this view of a woman's role, with varying degrees of confidence. Besides, approximately one in four who answered this question (24%) expressed complete agreement with this statement. On the other hand, about a quarter of respondents (26%) strongly or somewhat disagree with this statement. The results of the study can be used in international practice to overcome gender stereotypes. Social development of a person through higher education in digital environment plays a more critical role in overcoming gender stereotypes than previously thought.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".