Bibliographic record
Abstract
Canada, along with the rest of the developed world, has witnessed a precipitous drop in traditional defined benefit pension plan coverage. The result has been a shift to defined contribution plans and an increased reliance on personal savings vehicles. This process has effectively shifted the risk away from employers to the individual. The consequence of this shift is an individual-centric retirement model possessing heightened complexity and significantly higher costs. On average, the fees paid under a pension plan structure are 0.38% of the assets under management while comparable mutual fund fees average 2.1%. Although the numbers appear small, on a relative basis, this constitutes a cost difference of over five times. Ultimately, the cost difference results in a Canadian mutual fund investor having to delay retirement by over 7 years compared to a pension fund investor with the same investment performance. Does the mutual fund industry’s investment performance justify the significantly higher costs? Overwhelmingly, the academic evidence suggests no! In fact, the vast majority of active mutual fund managers consistently underperform a simple market index such as the S&P/TSX Composite Index. The Canadian retirement model has shifted towards a system where the dominant retirement vehicle consistently underperforms and aggressively over charges. In addition to unjustified fees, the individual-centric retirement model shifts the responsibility for investment decision making onto the individual. This requires the individual
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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".