Bibliographic record
Abstract
The WPHP Monthly Mercury is the podcast for the Women’s Print History Project (WPHP), a bibliographical database that seeks to provide a comprehensive account of women’s involvement in print in a long Romantic period. The podcast provides us with an opportunity to develop in-depth analyses of our data. The December 2020 episode, “1816 and 2020: The Years Without Summers,” explores women’s writing in the WPHP inspired by 1816, known as the Year Without a Summer, when abnormally cold weather, exacerbated by the aftermath of the Napoleonic Wars, led to crop failures and typhus and cholera epidemics. Often remembered as the cold and fog-laden year in which an 18-year-old Mary Shelley came up with the idea for Frankenstein, 1816 was a year of catastrophe more generally. In this episode, hosts Kate Moffatt and Kandice Sharren explore how the bibliographical metadata contained in the WPHP can uncover a wider range of voices writing about catastrophe. Our findings, which include political writing, travel memoirs, and poetry, reveal the lived experiences of women in a tumultuous time. We conclude by meditating on the nature of literary production during catastrophe, and how our own experiences during the upheavals of 2020 influenced our approach to the books that we uncovered.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".