Comparing Gender Differences in Computer Science and Management Information Systems Majors
Bibliographic record
Abstract
Why do so few women major in computer science (CS) or management information systems (MIS)? Are the reasons for women’s underrepresentation in these two disciplines the same? I will address these issues by reporting on my research on female and male undergraduates majoring in CS or MIS. I hypothesize that results from one area of information technology (IT), such as CS, do not necessarily generalize to other areas of IT, such as MIS. Structural barriers exist that keep women from advancement in IT careers (cf. Ahuja, 2002). However, an examination of these is beyond the scope of this article, which focuses on gender differences in IT students. In 2002 only 27.6% of U.S. Bachelor’s degrees in CS and 36.8% in MIS were conferred on women (U.S. Department of Education, 2002), limiting the number of women qualified to enter into the IT workforce. The problem of women’s under representation in IT is not limited to the U.S. Women are underrepresented in CS majors in other Western countries such as Canada (Gadalla, 2001), Germany (Huber, Reiff, Ben, & Schinzel, 2001), Norway (Berg, Gansmo, Hestflatt, Lie, Nordli, & Sorenson, 2002), and Ireland (MacKeogh, 2003). IT cannot afford to underutilize such a valuable and significant part of the workforce. Increasing the representation of women is imperative because “greater diversity among those who create computing technology ensures that those technologies are relevant to and usable by a wider range of people” (Roberts, 2003).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".