Cybersecurity as a Catalyst: Enhancing Accountability and Driving Change in Federal Agencies
Bibliographic record
Abstract
In an era of rapid technological advancements and increasing cyber threats, cybersecurity preparedness has become a critical component of organizational strategy, impacting the protection of information assets and overall organizational performance. This study examines the relationship between cybersecurity preparedness and key organizational outcomes, specifically accountability and effective changes in addressing challenges, within federal agencies. Data from the 2023 Federal Employee Viewpoint Survey (FEVS) were analyzed. Using descriptive statistics, spearman's rank correlation, ordered logistic regression, and structural equation model, the study assessed the impact of cybersecurity preparedness on organizational performance, controlling for gender, supervisory status, age, and tenure. The results indicate a significant positive association between cybersecurity preparedness and both accountability and effective changes in addressing challenges. Enhanced cybersecurity measures are linked to greater accountability and more effective organizational changes. These findings highlight the importance of robust cybersecurity strategies in improving organizational performance and resilience.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| 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".