Examining Barriers to Entry: Disparate Gender Representation in Cybersecurity Within Sub-Saharan Africa
Bibliographic record
Abstract
Globally, women are underrepresented in the fields of Science, Technology, Engineering and Mathematics (STEM). In Sub-Saharan Africa (SSA), this underrepresentation is even more prevalent, as fewer women pursue STEM careers in SSA when compared to the global norm. Cybersecurity is a critical subsection of STEM; one that is widely accepted as a field with enormous growth potential, yet only a small proportion of these jobs belong to women. Despite attempts to narrow the gender gap in cybersecurity, persistent factors still contribute to this disparity. Within this field, developing countries struggle with the same issues that impact their more developed counterparts. Issues that impact both SSA and the global participation of women in cyber-security include lacking representation and awareness as well as retention problems. Further, issues such as harassment, gender bias and the idea that cybersecurity is a “man’s world” are also contributing factors. A slew of other factors is also at play in SSA; this includes issues of low school attendance by girls, restricted educational opportunities, and other systemic challenges. Girls and women are less likely to complete lower and secondary education, which has a ripple effect – fewer women reach higher education in SSA when compared to global trends. Generally, higher or tertiary education is necessary to join the cybersecurity workforce. Research exploring the challenges women in SSA face when trying to enter the cybersecurity field is limited. This paper presents an overview of the most persistent challenges faced in SSA and globally. It highlights the current skill shortage in the cybersecurity field that is exasperated by global challenges, including issues unique to the region. Educational pathways available to girls and women are explored, as well as the issues leading to widespread skill shortages within SSA. Programs striving to increase the participation of women in cybersecurity are discussed. Lastly, some suggestions to remediate this pervasive issue are also provided.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".