Bibliographic record
Abstract
Greed and fear.Many people cite these as the two main driving forces behind the stock market.But a successful investor should know that qualities such as vision, knowledge, discipline, risk tolerance and patience are much more important.John W. Dobson mastered all of these qualities to become one of Canada's greatest growth-stock investors.He launched his Formula Growth Fund on 27 June 1960, with a small group of investors who put up a total of $134,000 (CAN), including $20,000 from Dobson, to buy units in the fledgling fund.John's initial investment would have been worth some $24 million as of 2020 -an increase of an incredible 1,200 times!This represents a compounded rate of return of 12.67 per cent a year over roughly sixty years of successful investing.In contrast, the same amount invested in the S&P 500 Total Return index would have been worth just $9.2 million (CAN).This is the kind of performance that has earned the long-term loyalty of Formula Growth clients, who include some of Canada's most prominent businesspeople and entrepreneurs.It has also drawn the attention of several of the world's savviest investors, such as the late Sir John Templeton, who at one time was one of the largest Formula Growth Fund unit holders. Memo to staff from John Dobson:Objectives of Formula Growth Limited: 1.To make 20 per cent per year for our unit holders 2. To have fun doing it 3.To make certain that we have the people, discipline, and procedures to accomplish objectives 1 and 2
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.640 | 0.474 |
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".