Long Term Availability of Primary Research Materials in Popular Romance Studies
Bibliographic record
Abstract
Presented at Northeast Popular Culture Association 2024 Hybrid Conference This study is a response to Allan’s 2023 article in the Journal of Popular Romance Studies where he asked “about the future of scholarship, what happens if those primary texts we study are inaccessible to a future researcher? How should the field of popular romance studies begin the process of archiving the primary materials that are studied and talked about?” Publishers have historically published print catalog romance titles for short runs, releasing new titles frequently. While these titles are not available on the market for very long, the print format lends stability to them being availability in public and private libraries once they are collected. Popular Romance genre was one of the first to embrace the ebooks publishing format in the last 20 years, but there are no physical items to collect for electronic-only published titles. Onset of self publishing platforms like Kindle Direct Publishing have only accelerated the release of new titles. These e-only titles are often distributed outside of traditional library acquisitions methods and license only access models adds additional complexity. Academic libraries have yet to address these issues so we can build stable long term e-only fiction collections. In this study, I present the availability of primary source materials cited in 40 years of published popular romance studies for Canadian academic researchers via libraries and vendors. Using this study as a proxy to the state of romance collecting of our past, I will explore how we can address any gaps today and plan for the future.
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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 | 0.043 | 0.236 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.067 | 0.016 |
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".