Building a Skilled Cyber Security Workforce in Five Countries
Bibliographic record
Abstract
Cyber security breaches continue to significantly threaten governments, businesses and individuals worldwide.The demand for cyber security professionals has increased significantly in recent years around the world and is expected to continue to grow, and this trend has created shortages in labour markets in several countries.The first step in addressing skills shortage in the cyber security sector is to understand the supply and demand dynamics of cyber security skills.This information can be used by governments and organisations to identify their vulnerabilities and determine where additional resources are needed.By analysing job postings, trends in demand for cyber security professionals and the skills for creating a secure organisational environment can be identified.Meanwhile, studying the provision of cyber security education and training programmes provides insights into how the cyber security workforce is being developed and the potential misalignment between demand and supply.This report analyses the demand for cyber security professionals in five countries (Australia, Canada, New Zealand, the United Kingdom and the United States), and zooms in on the provision of cyber security education and training programs in England (United Kingdom).The report aims to provide a comparative analysis of cyber security demand in the five countries, with a detailed analysis of the education and training programmes and policies put in place in England to make the profession more attractive and diverse.The report is the first in a series of studies that aim to expand knowledge on the cyber security workforce and related education and training provision in various regions and countries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".