HAS BARRICK BEEN BARRICKED BY THE U.S.?
Bibliographic record
Abstract
dustbin its long-standing hedge policy, and pay for buying back its hedge-book by diluting the value of its common stocks through issuing more than 81 million new shares, or about 10 percent of the outstanding. The so-called hedges of Barrick have been thoroughly discredited and will soon be history. So-called, because the long-term forward sales contracts in question that the parvenu gold miner has invented and flaunted are not proper hedges and never have been. They are a fraud. They are naked short positions pretending to be balanced by gold ore reserves in the moon (or on this earth which, for hedging purposes, is practically the same thing). Part of the newsworthy story, of course, is the fact that the hedge book of Barrick has been increasingly under water for some nine years now, threatening the unfriendly giant with drowning. Within 24 hours another hasty announcement was made to the effect that the company, instead of issuing 81 million new shares, will in fact issue 94.4 million, that may be raised to 109 million if the demand justifies it, for a total value of $4 billion — the biggest primary equity offering in Canadian history according to the local media. The hike was explained by “strong investor demand”. The market, however, put a big question mark to that “forwardlooking
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".