Bibliographic record
Abstract
Rockefeller’s global reach -- from the University of Chicago to the Holy Land. \n \nFROM THE ARTICLE: John D. Rockefeller knew a thing or two about power. His Standard Oil of New Jersey became a blueprint for corporate centralization. He pioneered new methods of stock rigging and financial mischief. He destroyed competition wherever he could and set new standards for industrial sabotage and union busting. He manipulated the tastes of “rational consumers” and made “policymakers” dance to his tune. He used violence to expropriate from indigenous Americans their resource-rich lands, and religion to pacify their resistance. He harnessed the U. S. military to impose American “free trade” on the rest of the world. Raw power made Rockefeller and his family enormously rich. And yet, to the end of his life, John D. insisted that his best investment ever was the $45 million he donated to rebuild the Baptist University of Chicago. Rockefeller saw Chicago as a religious asset. The philanthropy helped silence his critics in this world and pave his way to heaven in the next. It bought him the loyalty of spiritual shepherds and academic retainers, all eager to sing the praise of Standard Oil and glorify its devout owner. But in the long run the biggest yield came from the university’s department of economics. \n \n[. . .] \n \nA FEW WORDS ON THE HISTORY OF THIS ARTICLE: The paper was originally commissioned in August 2003 by the Journal of Cold War Studies. Following our explicit inquiry, the journal confirmed that our text would be published “as is.” With this assurance, we submitted the paper in January 2004. The paper was longer than the journal’s standard review. We explicitly drew attention to the extra length and explained why a longer article was necessary given the subject matter. The journal accepted the review and scheduled its publication to the Fall of 2004. \n \nBut then the editor, Mark Kramer, had a change of heart. Having read our paper, he must have realized he had made a big mistake. This type of criticism had no place in his respectable journal. He began evasive actions. Without notice, our paper was postponed to the next issue, and then to the following one. We protested the censorship. Kramer assured us there was none. There was simply a long backlog of reviews, he said. Our paper would be published as is and without editorial intervention. \n \nFinally, in April 2005, the truth came out. We were notified that the paper would not be published at all. It was simply . . . too long. We could, if we wanted to, cut the article in half. Or, alternatively, we could enlarge it into a review essay and re-submit it to the journal’s referees. But then the Journal would have to re-consider it. . . .
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.325 | 0.123 |
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".