Peculiar Features of Attaining the CERA Designation in Canada
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The article sets out peculiar features of attaining the CERA credential from the Canadian Institute of Actuaries. The analysis results show that there are scores of ways available to candidates perusing the aim of becoming a CERA. There have been singled out the main models following which it is possible to get the designation which is administered by the following professional bodies: the pathways proposed by the Institute and Faculty of Actuaries, the German Society of Actuaries and European Actuarial Academy, the Netherlands and France exam systems, and finally the examinations and modules offered by the SOA. Since there are no examinations and modules leading to the designation administered solely by the CIA, the research is basically focused on the analysis of the standards laid down by the CAS and SOA which are the Institute’s closest partners offering the requirements completion of which results in attaining not just the CERA, but also the ACIA and FCIA credentials. Having analyzed the SOA’s CERA examination systems, we have figured out that for candidates willing to become CERAs there are only two ERM specific activities: the ERM exam and module. We may conclude that there is a considerable overlap between the two, only the exam section dedicated to extensions being different. The CAS system is organized on the basis of cooperation with the Institute and Faculty of Actuaries meaning that on the pathway to the designation awarded by the CAS candidates have to complete the Risk Management Specialist Technical Exam which is commonly abbreviated to ST-9. The second ERM specific requirement is the completion of the Enterprise Risk Management and Modeling Seminar. A two-fold nature of the CERA requirements in the CAS case is utilized to a more efficient extent as it adds an interactive component and an opportunity to exchange experience with the actuarial practitioners working in the field of ERM.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it