Dear Mr.Kingston: TO BARRICK OR TO BE BARRICKED, THAT IS THE QUESTION
Bibliographic record
Abstract
Thank you for writing. You ask me whether Barrick is a 'buy ' now that it has reached its long-time ambition of becoming the first: the largest gold producer of the world. Would it now start moving up from its place of being the last: the world's worst large-cap share price-performer in the gold-mining business? I am a monetary economist and as a rule do not offer investment advice. Having said that, the name "Barrick " touches a raw nerve in me. I used to be a shareholder. In 1992 I took early retirement from my professorship, accepting bribe money (they call it 'golden handshake') from Memorial University of Newfoundland, my academic home for 35 years. At stake was about $50,000 which I invested in Barrick shares and leaps, with the idea of arbitraging one against the other. As the gold price went up, I would sell leaps and put the proceeds into shares, and vice versa. Most of this investment has gone up in smoke as a result of Barrick's 'Brave New World of Hedging'. I decided that, in reply to your kind letter, I would tell my story. The Godfather Barrick's founder and godfather, Peter Munk, like myself, grew up in Budapest.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.096 | 0.058 |
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".