Adding gender to the archival contextual turn: the Rocky Mountain photographic records of Mary Schäffer Warren
Bibliographic record
Abstract
This thesis explores the significance of gender as an overlooked element of context in understanding the provenance of archival records. The relevance of gender to archival provenance is demonstrated through a case study analysis of the gendered contexts of record creation, use, and meaning. The analysis is grounded in an examination of the archival photographic and textual records of Mary Schäffer Warren, an amateur photographer, traveller, and explorer of the Canadian Rocky Mountains during the years 1888 and 1939. This thesis argues that gender is an important context in a record’s provenance providing nuanced understandings of socio-cultural relations and processes of record creation, use, and meaning. Gender as context further empowers the principle of provenance by more fully reflecting how and why records are created which accordingly allows archivists to appraise, acquire, and describe records in ways more sensitive to gender as a socio-cultural reality.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.015 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".