Bibliographic record
Abstract
And so, after a detour through the spectacle of finance – the public theatre of prices, the rhetoric of financial maths, stories of bond-jamming ‘big swinging dicks’ and corporate-raiding buccaneers, anxious oddballs piloting arcane equations while calm women sooth their fevered brows – it is time to bring the 1980s to a close. There is no better way to do so than with another story, of a pantomime villain and larger-than-life character, Tom Wilmot, London’s very own Wolf of Wall Street. Wilmot became a household name in 1985 after publishing a bestselling introductory guide to the UK’s over-the-counter markets. Trading OTC, as it is commonly known, simply means that the stock has not been admitted to any market but is traded by the broker’s firm. In avoiding admission to a market, however, stocks bypass one of the biggest quality controls that protects investors. Wilmot’s firm went by the reassuring, stolid name of Harvard Securities. According to his book, Harvard acted in ‘dual-capacity’, dealing what it happily described as ‘speculative share issues’. This fact is crucial. Harvard Securities not only sold stock to the newly propertied Sids of the mid-1980s, but also made the market in those stocks . In the last few chapters we have seen how prices contain the power relations of the context of which they are assembled, and how, in order to claim legitimacy, they must be made in some kind of daylight. Harvard was better informed, better capitalized and better staffed than those who purchased its shares, yet the broker itself was opaque, the spectacle of public proof very much absent from its dealings. The firm was founded in 1973 by a Canadian named Mortie Glickman; Wilmot refers to him in his book as ‘Mr M.J. Glickman’. It later emerged that Mr M.J. Glickman had what journalists call a ‘colourful background’. Working with a man named Irving Kott, he had set up a broker named Forget in Montréal. The company made a living employing high-pressure telephone sales to push stocks in dodgy Canadian companies onto European investors; much of the work was done through a Frankfurt-based operation, also set up by Kott and Glickman.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.325 | 0.212 |
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".