Experiences of current and former members of self-managed superannuation funds
Bibliographic record
Abstract
We surveyed 854 current and 147 former members of self-managed superannuation funds (SMSFs) in 2016. The results of our survey document their aspirations, operational practices and experiences. Both current and former members expressed high general interest in superannuation, but ‘detractors’ of SMSFs outnumbered ‘promoters’. SMSF members said they enjoy ‘control’ of investment, but a majority delegated tasks to financial professionals. Three times as many members rated the performance of their fund as above the SMSF average as below, although most did not measure the performance of their fund adequately. The probability of closing a SMSF is significantly higher if members use net returns, rather than other indicators such as account balance, to judge performance.JEL Classification: <b>H55, H75, J32</b>
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".