The submission was made by Mr Vladimir Menkov 1 FOREIGN SUPERANNUATION FUNDS: NORTH AMERICAN EXPERIENCE Honorable Members of the Committee:
Bibliographic record
Abstract
A non-citizen has, of course, no right to comment on proposed changes to Australian laws. Nonetheless I felt that it may be useful to bring some North American perspective to the current Parliamentary inquiry on transfers from overseas superannuation funds. There are thousands of people in Canada and the USA, often in the middle of their careers, and already having accumulated substantial retirement savings, considering migrating to Australia permanently or temporarily (but for a long enough term to become residents for tax purposes). There are perhaps even more Australian citizens working in North America and contributing to US and Canadian retirement plans, and considering eventually coming back to Australia. For example, according to the US INS statistics [1], just in one fiscal year (1999), more than 14,000 Australian citizens entered the USA just on temporary employment or self-employment visas (H1, H2, L1, and E1); this implies that the total professional population of Australians in the USA, on temporary or permanent visas, is tens of thousand of people; most of them have US retirement accounts. BACKGROUND What kind of superannuation plans do people working in North America contribute to? How could a wise government treat their retirement savings upon moving to Australia, so as not to discourage people from such
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.032 | 0.021 |
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".