Bibliographic record
Abstract
Abstract — The security community has used psychological research on attacker personalities, but little work has been done to investigate the personalities of the defenders. One instrument currently dominating personality research is the Five Factor Model, a taxonomy that identifies five major domains of personal traits, composed of sets of facets. This model can be used within an organizational or vocational capacity to reveal dominant tendencies, such as openness to new experiences. Within a security context, this tool could show what patterns professionals exhibit, which may reveal areas of insufficient diversity and “blind spots ” in defenses. We surveyed 43 security professionals using a Five Factor Model-based test (the IPIP-NEO) to reveal common dominant traits. We found that our sampled security population demonstrated that they were highly dutiful, achievement-striving, and cautious; in addition, they were high in morality and cooperation, but low in imagination. We note that many of these characteristics seem to be appropriate for security professionals, although the low scores in the “openness to experience ” domain may indicate difficulties in devising new security defense methods and in anticipating new forms of attack. This finding implies that security professionals might be more reactive to security threats, rather than proactive in discovering them before they are used by adversaries. This lack of anticipation could potentially leave large organizations vulnerable to attacks that might have otherwise been prevented. I.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".