Restoring Women's History through Historic Preservation
Bibliographic record
Abstract
Contents: Acknowledgments CHAPTER ONE Restoring Women's History through Historic Preservation: Recent Developments in Scholarship and Public Historical Practice Gail Lee DubrowPART I Documenting the History of Women in PreservationCHAPTER 2 Women in the Nineteenth-Century Preservation Movement Barbara J. HoweCHAPTER 3 Special Places Saved: The Role of Women in Preserving Shaun EyringCHAPTER 4 Four African American Women on the National Landscape Faith Davis RuffinsPART II Revisiting Women's Lives at Historic Houses and MuseumsCHAPTER 5 Uncovering and Interpreting Women's History at Historic House Museums Patricia WestCHAPTER 6 Domestic Work Portrayed: Philadelphia's Restored Bishop William White House-A Case Study Karie Diethorn with John BaconCHAPTER 7 Putting Women in Their Place: Methods and Sources for Including Women's History in Museums and Historic Sites Edith MayoPART III Claiming New Space for Women in the Built Environment and Cultural LandscapeCHAPTER 8 Rooms of Their Own: The Nurse's Residences at Montreal's Royal Victoria Hospital Annmarie AdamsCHAPTER 9 On the Inside: Preserving Women's History in American Libraries Abigal A. Van SlyckCHAPTER 10 Women in the Southern West Virginia Coalfields Susan M. PierceCHAPTER 11 Night with Venus, a Moon with Mercury: The Archaeology of Prostitution in Historical Los Angeles Julia G. CostelloPART IV Exemplary ProjectsCHAPTER 12 The Power of Place Project: Claiming Women's History in the Urban Landscape Dolores HaydenCHAPTER 13 Best Practices for Saving Women's Heritage Nonprofit Case Studies Jennifer B. GoodmanCHAPTER 14 It's a Wide Community Indeed: Alliances and Issues in Creating Women's Rights National Historical Park, Seneca Falls. New York Judith WellmanCHAPTER 15 Raising Our Sites: A Pilot Project for Integrating Women's History into Museums Kim MoonCHAPTER 16 Finding Her Place: Integrating Women's History into Historic Preservation in Georgia Leslie N. SharpCHAPTER 17 Blazing Trails with Pink Triangles and Rainbow Flogs: Improving the Preservation and Interpretation of Gay and Lesbian Heritage Gail Lee DubrowPART V Toward and Inclusive Agenda for Preservation Policy and PracticeCHAPTER 18 Searching for Women in the National Regier of Historical Places Carol D. ShullCHAPTER 19 Reflections on Federal Policy and Its Impact on Understanding Women's Past at Historical Sites Page Putnam MillerCHAPTER 20 Parks Canada and Women's History Alan B. McCulloughAFTERWORD Proceeding from Here Heather A. HuyckAppendix A: Participants in the Pilot of Raising Our Women's History in PennsylvaniaAppendix B: Women, Women's Organizations, and Buildings Related to Women Which Have Been Commemorated by the Canadian Minister Responsible for National Historical Sites Notes List of Contributors Index
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".