“He Has Ideas about Everything”: An Introduction to the Franklin Ford Collection
Bibliographic record
Abstract
Has Ideas about Everything": An Introduction to the Franklin Ford Collection 2This collection would not have been possible without the hard work of Amandine Hamon and Simona Feng, to whom we offer our warmest thanks.On April 13, 1886, a lively debate took place before the members of the Nineteenth Century Club in New York.During a conference discussing the press, one participant asserted the surprising opinion that the newspapers were not as good as those of fifty years before. 1 At the dawn of the Progressive Era, such beliefs were not shared by the majority, and were certainly not common among journalists.For the first time in history, an extensive coverage of fresh international news was possible, thanks to the cables of the Associated Press and the like.Reporting was becoming a self-conscious and esteemed occupation in American cities, and reporters were generally greeted with kudos, as readers enjoyed the exotic adventures of the many star journalists and "girl stunt reporters" of the era.The surprising comment came from the mouth of Franklin Ford (1849-1918), the editor of the Bradstreet's Journal of Trade, Finance, and Public Economy.A seasoned newsman, Ford was then embarking on a long reflection on journalism, media, and communication.Over the next three decades, he gave conferences, published essays, and discussed his ideas with many high-profile correspondents, including Supreme Court Justice
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.068 | 0.025 |
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".