Dear Mr.Kingston: TO BARRICK OR TO BE BARRICKED, THAT IS THE QUESTION
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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 it