The North American Society for the Study of Romanticism 2014 Conference Website
Bibliographic record
Abstract
The NASSR 2014 website was dedicated to the July 2014 conference of the North American Society of the Study of Romanticism (NASSR), jointly organized by Patrick R. O’Malley in the Department of English at Georgetown University and Richard C. Sha in the Department of Literature at American University. According to the website for the North American Society for the Study of Romanticism (www.nassr.ca, available at the date of this archive’s creation in 2024), it “was founded at the University of Western Ontario in 1991 by a group of faculty members and graduate students. NASSR was established to provide a forum for the discussion of a wide variety of theoretical approaches to Romantic works of all genres and disciplines. NASSR members from North America, Asia, Europe, and Australia work in a wide range of disciplines, including History, Art History, Women's Studies, Philosophy, Music, Political Economy, and Literature; their interests encompass American, Canadian, English, French, German, Irish, Italian, Russian, Scottish, and Spanish Romanticism.” The 2014 conference, NASSR’s 22nd annual meeting, was on the theme of “Romantic Organizations,” and it welcomed over 375 registrants and comprised almost 60 panels and seminars, as well as trips to the National Library of Medicine and the Library of Congress. Keynote speakers were Peter Dear of Columbia University, Marshall Brown of the University of Washington, and Elizabeth Fay of the University of Massachusetts, Boston.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.477 | 0.260 |
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".