The Verification of the Social Insurance Premium Reducing Effect by the Conclusion of the Social Security Agreement: \nThe Case of Japanese Companies in Canada
Bibliographic record
Abstract
Employees dispatched abroad must join the social insurance systems in both their home and partner countries, which raises the problem of double burden of social insurance premiums. It is the social security agreement that is concluded between the two countries to avoid this problem. With the social security agreement, companies do not have to join the social security system of the other country, eliminating the double burden of social insurance premiums. This article estimates how much the social insurance premium of Japanese companies has been reduced by the social security agreement between Japan and Canada (signed in 2006 and effective in 2008). Canada’s public pension system (old-age pension) consists of the Old Age Security (OAS) and the Canada Pension Plan (CPP). OAS is funded by taxes and provides a fixed amount of benefits. CPP is funded by social insurance premiums and provides income proportional benefits. Only Quebec has its own Quebec Pension Plan (QPP). QPP is not covered by the JapanCanada Social Security Agreement. The estimation result is as follows. With the conclusion of the Japan-Canada Social Security Agreement, the amount of social insurance premiums that Japanese companies in Canada could reduce in 2016 was approximately 996 million yen.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".