IT education as a factor to influence gender imbalances in computing: Comparing Russian and American experience.
Bibliographic record
Abstract
Introduction. The problem of the relatively small number of women professionally employed in computing (computer science and information technology) is relevant throughout the world. Despite the fact that IT professionals are widely in demand, women in many countries, including theUSA andRussia, make up no more than a quarter of their total number, which requires explanation. One of the major reasons for this phenomenon, according to the authors, lies in the education system. The aim of this article was to analyse the factors affecting gender imbalance in IT professions, by comparing two countries in which information technology has historically played an important role, and which are very different from each other in many ways – economic, political, educational system and others. Research methodology. The present research is based on the comparison of data on IT education in schools and universities, and the degree of involvement of girls and women in computing in theUSA andRussia. Results. Both in theUSA and inRussia, gender imbalances in IT professions are formed largely in the field of education. Cultural stereotypes about computing as a male-dominated profession are produced by the media. Such stereotypes can discourage some girls and young women from studying computer science and also result in imbalance formation. The education system needs to increase the confidence of girls and young women in the possibilities of realising their abilities in the field of computer science and information technologies. Educational institutions should help to eliminate the negative attitude towards girls’ choice of IT professions. Scientific novelty. For the first time, general factors in the field of education were identified that affect gender imbalances among IT professionals inRussia and theUSA – the countries with significantly different traditions and educational systems. Practical significance of the present work is to justify the conditions for improving school and university education to solve the problem of gender inequality in IT industry.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".