Bibliographic record
Abstract
One of the wildest, most spectacular decades in American history, the 1920s were a period of unprecedented growth and mass consumerism. In the New Era, people drank in speakeasies, danced to jazz, idolized gangsters, and bet their life savings on stocks. Born and raised in a small Canadian town, Arthur Cutten went to Chicago in 1890 with ninety dollars to his name. Through utter ruthlessness, he amassed a fortune trading in grain futures and stocks. Cutten was heralded as the modern Midas, and his every move was followed by the masses, who believed they could get rich quick. But everything changed after the crash of 1929. The heroes of prosperity became the villains of the Great Depression. Determined to crack down on the “banksters,” the Roosevelt administration launched an all-out attack on those it blamed for the collapse – and Cutten was at the top of the list. A US Senate committee probed how he manipulated stock prices. The Grain Futures Administration moved to bar him from trading. And the Bureau of Internal Revenue indicted him for income tax evasion. But the wily operator won on every count: he emerged from the Senate investigation unscathed, maintained his grain trading privileges after a victory in the Supreme Court, and left almost nothing for the tax collectors upon his death. To Make a Killing tells the tale of Cutten’s journey to fabulous wealth, the forces that propelled him, and the fascinating characters in his life.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".