Bibliographic record
Abstract
Mary Schäffer was a photographer, writer, botanical painter, and mapmaker from Philadelphia, well known for her travels in the Canadian Rockies and Japan at the turn of the twentieth century. In Searching for Mary Schäffer, Colleen Skidmore takes up Schäffer’s own resonant themes—women and wilderness, travel and science—to ask new questions, tell new stories, and reassess the persona of Mary Schäffer imagined in more recent times. Public and private archival collections in the United States and Canada set the stage for this engrossing exploration of Schäffer’s creative, collaborative, and competitive enterprise amid the cultural complexities of Philadelphia’s science and photography communities, and the scientific, tourist, and Indigenous societies of the Rocky Mountains of Canada. “In this impressive book, Colleen Skidmore uses her considerable skills as a social historian of photography to shed new light on the remarkable life of Mary Schäffer. She knows the stories, the characters, and presents a social history that is fresh and convincing. Skidmore’s conclusion is brilliant and will certainly serve as a catalyst for further research and study of Mary Schäffer.” Donna Livingstone, President and CEO, Glenbow Museum
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 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".