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 Dobson mastered all of these qualities to become one of Canada's greatest growth-stock investors.Over the past fifty years, a unit in his Formula Growth Fund has grown in value from the equivalent of $9 (CAN) to more than $5,400.An initial investment of $10,000 in Formula Growth in 1960 is worth today more than $6 million (CAN).In other words, it has increased an incredible 600 times!What's more, Formula Growth has lost none of John Dobson's magic.As this book went to print, according to The Globe and Mail's Globe Investor, the fund ranked number one among Canadian investment firms managing US small-to mid-cap stocks, with a trailing one-year performance of 46 per cent through 30 September 2013.To increase 600 times is one thing, but to still be top of class after fifty-three years is priceless.It is the kind of performance that has earned the long-term loyalty of Formula Growth Fund unit holders, among them some of Canada's most prominent business people and entrepreneurs.It has also drawn the attention of several of the world's savviest investors, including the late Sir John 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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.638 | 0.477 |
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".