Do governance determinants contribute to effective management of cybersecurity threats posed by misleading information? Evidence from Canadian organizations
Bibliographic record
Abstract
Purpose This study aims to explore governance solutions adopted by Canadian organizations to address the cybersecurity threats posed by misleading information. Design/methodology/approach This paper investigates the impact of several organizations’ governance determinants on five types of misleading information: phishing incidents, impersonation, fake news or false stories, fake reviews and other types of misleading information. Using nonparametric statistical techniques and regression models, this study assessed regional variations in responding to misleading information challenges and the effectiveness of mitigation strategies. Findings These results reveal that no unique governance solutions have emerged across the jurisdictions, implying that organizations operating in each province have different tolerances for emerging cyber risks and, thus, adopted specific strategies to combat them. The results also suggest that the impact exerted by specific governance determinants on misleading information varies across jurisdictions. Research limitations/implications Limitations: The reliance on secondary data may limit the generalization of the results to other countries. Future research should consider additional determinants, such as non-technological organizational factors, and a longitudinal approach to assessing the significance of solutions and the frequency of incidents. Implications: The study’s findings are expected to contribute to operational and strategic directions that elevate awareness of the growing threat of misleading information in the cyber domain. It provides stakeholders with effective governance solutions that play a critical role in mitigating cybersecurity risks by fostering awareness and detection capabilities. Practical implications The study’s findings are expected to contribute to operational and strategic directions that elevate awareness of the growing threat of misleading information in the cyber domain. It provides stakeholders with effective governance solutions that play a critical role in mitigating cybersecurity risks by fostering awareness and detection capabilities. Originality/value This paper offers new insights and practical implications about governance solutions that might be considered in combating specific misleading information portrayed as emerging cyber threats.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".