Bibliographic record
Abstract
L’objectif de cette étude vise à comprendre comment les compagnies d’assurance Canadienne conceptualisent les cyber risques afin d’être en mesure de quantifier des pertes résiduelles ou en constante évolution. Par l’entremise de 10 entretiens qualitatif avec des professionnel de l’assurance, nous avons trouvé que la souscription à une cyber assurance peut aider les entrepreneurs à gérer les risques causés par la cyber criminalité. L’étude montre que la cyber assurance contribue à la compréhension et à la diffusion de connaissance en matière de cybercriminalité. Ceci est facilité par la recherche continue sur le phénomène et de la mise à jour ces polices d’assurance. Aussi, il a été trouvé que les professionnels de l’assurance facilitent l’application des mesures de prévention cyber. Cette gestion est permise grâce aux outils mis à disposition des assureurs afin d’évaluer les composantes de sécurité pour contrer les cyber attaques. Finalement, la recherche démontre que le milieu des assurances joue un rôle d’envergure dans la surveillance et la gouvernance des cyber risques.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".