Supporting Exploration of Women’s Print History Project Data via Interactively Constructing Networks of Interest
Bibliographic record
Abstract
We designed, developed, and studied a visualization, WPHPVis, to support exploration of the Women’s Print History Project (WPHP) data. WPHP are manually-collecting a bibliography that spans the years 1700 to 1836 recording information about books in which women have been involved through a number of roles including as authors, editors, translators, publishers, printers and booksellers. By working directly with WPHP experts to focus on their understanding, and their research practices and needs, we co-designed WPHPVis using interactive construction of network links to support exploration of their data. Through our qualitative study with both experts and non-experts, we learned about how the tool supported the WPHP experts’ research practices as well as about how to improve overall interactive experience. We conclude by discussing the importance of representing missing data, the advantages of striking a balance between visualization structure and explorability, and the opportunities enabled by co-design with domain experts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".