Bibliographic record
Abstract
Cybersecurity is a critical concern in contemporary digital environments, especially within the context of complex, interconnected systems. This study presents a systematic review of the complexities and inconsistencies surrounding the use of cyber-related terminology. A two-phased approach. The first part entailed using the PRISMA model to find relevant material, which was analyzed using ATLAS.ti software during the second phase. The analysis reveals ambiguity in cyber-related constructs, such as ‘cybersecurity’ versus ‘cyber security’, which impacts the clarity of research, policy development, and organizational practices, including education and training. Additionally, the study identified ‘cybersecurity’ as a primary security concern, interconnected with secondary and tertiary constructs. These relationships, visualized through ATLAS.ti Sankey diagrams, provide insight into how cyber-related constructs; all interrelated within the broader cyber ecosystem and within the dataset used for the study. This research is interesting and relevant because it clarifies the inconsistent use of cyber-related constructs and thus, each narrative constructed around cyberspace and its security. These taxonomic clarifications are also useful additions to curricula offering education and training in cyber-related subjects. Furthermore, organizations delivering security and/or intelligence services, within the context of cyber-related functional applications, can use such clarification to enhance their education, training materials, and functional environments.
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 | 0.015 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.026 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".