Digital Romanticism in the Age of Neo-Luddism: the Romantic Circles Experiment
Bibliographic record
Abstract
The Romantic Circles Website, along with a number of other major projects in digital Romanticism, came online around 1995, a historical moment that also saw the emergence of neo-Luddism, in part as a reaction to the techno-hype of the Internet boom. At the time. neo-Luddites often claimed as a precedent the original historical Luddism of 1811-16, but they usually also Romanticized that collective labor subculture to fit their own late-twentieth-century ideas of “technology.” This essay looks back at the interlinked assumptions in the air around 1995–neo-Luddite and Romantic–as the context out of which Romantic Circles defined its own engaged experiment in technology. Iw ill cite specific examples of digital technologies from our first year (two editions about technology, including the technology of texts), and one from our most recent year (an experiment in podcasting), in order to explain how we at Romantic Circles have attempted to work at the crossroads of Romanticism and technology, while stubbornly refusing to play the role of "natural Luddites,"
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.031 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".