Bibliographic record
Abstract
In 1960, Montreal money manager John Dobson launched an informal investment club with a close group of friends and associates, including future prime minister John Turner. His Formula Growth Fund would go on to become one of North America's most successful investment funds, consistently outperforming the Dow Jones Industrial Average and attracting the likes of legendary investor Sir John Templeton. Up and to the Right tells the story behind John Dobson's investment success as well as his many contributions to entrepreneurial education. Craig Toomey provides valuable insight into Dobson's unconventional but disciplined investment approach, his uncanny ability to predict winning stocks, and his unwavering faith in the stock market despite its many ups and downs. Coinciding with the sixtieth anniversary of the Formula Growth Fund, this revised edition brings the company's fascinating story up to date. It presents additional investment case studies and explains how, through the launch of a successful hedge fund platform and expansion into Asia, Formula Growth has tripled its assets under management since the first edition was published in 2014. An initial investment of $10,000 made in 1960 has grown in value to an astonishing $12 million - a return on investment that speaks to Dobson's legacy. Based on interviews with Dobson as well as with dozens of members of his extensive network of friends, colleagues, and investment professionals, Up and to the Right is a fascinating story about a great Canadian who believed deeply in self-reliance and free enterprise as well as the value of friendship, pursuing one's passions, and working for the greater good.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.108 | 0.057 |
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".