Bibliographic record
Abstract
Franklin Ford (1849–1918) is mostly known for his association with the philosopher John Dewey in the late 1880s and early 1890s. Together, they attempted to launch Thought News, a “philosophical newspaper” that never saw the light of day. But both before and after that failed project, Ford never stopped developing a vision for the future of the news. Reading Ford is a jumping-off point for experimentations that raise original methodological questions in the field of media history and theoretical developments that speak to contemporary media problems. In that regard, our paper focuses on the methodological experiment undertaken to explore Ford’s work: the creation of an automated Twitter account, a “bot” that uses text-mining techniques to automatically tweet excerpts from his writings. The paper describes the concrete steps of that remediation: from the delineation of Ford’s written work to the gathering and digitization of the material and its transformation into tweetable soundbites. We argue that this combination of close and automated reading offers heuristic elements of surprise to guide the historical inquiry. As the tweets echo the specific genre of today’s “future-of-the-news” thinkers, they also constitute an attempt to explore the relationship between “old” and “new” media.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.828 | 0.629 |
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".