Bibliographic record
Abstract
The WPHP Monthly Mercury is the podcast for the Women’s Print History Project, 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. In the August 2020 episode, “Black Women and Female Abolitionists in Print,” the entire team of the WPHP joins hosts Kandice Sharren and Kate Moffatt to speak to the Black Women’s and Abolitionist Print History Spotlight Series published on the WPHP website between June 19 and July 31, 2020. The WPHP team cameos are followed by a discussion between the hosts about the common themes of the spotlights produced. We then analyze some of the common threads across the spotlights, including how the people, firms and titles featured were documented and framed within a predominantly white transatlantic print culture. We conclude by considering some strategies for working within the constraints of the resources that we rely on and adapting our own data model to be more transparent and inclusive. This textual supplement includes a description of the episode, links to all records in the WPHP database referenced in the episode, resources relevant to this topic, our works cited list, and suggestions for further reading.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".