ATTITUDES OF SIXTH FORM FEMALE STUDENTS TOWARDS THE IT FIELD
Bibliographic record
Abstract
It is well-known that girls are not interested in computer science, information systems (IS) and software engineering studies. While the underlying reasons for this phenomenon have been studied in the US, Canada and Australia, only a few studies have been carried out in Europe and none in Scandinavia. To fill this gap in the research, we have analyzed the qualitative responses of 64 female sixth-form students concerning their attitudes towards studying information technology (IT), including Computer Science, Information Systems and Software engineering disciplines, and their views on IT as a profession. The results suggest that the IT field is seen in quite a positive light by the girls. Although many of the respondents do not consider IT to be their profession, they nevertheless have positive attitudes towards the field. According to the respondents, the field is growing and developing; it is respected, and seen as the field of future. Girls who want to become IT professionals see that the profession entails good employment possibilities and benefits and is respected. Some girls have negative views towards the field. These views illustrate the underlying reasons that these girls do not want to study IT. These girls did not perceive the field to be human-related (the work is only computer-related, according to the respondents). The need for skills in mathematics and physics are also listed as key reasons that some girls do not want to become IT students The results of the study suggest that there is a need to clarify among sixth form students the fact that
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".