Permission to feel: Refocusing provenance on emotions using the photographs of the Dave White family fonds
Bibliographic record
Abstract
The study of emotions has attracted the interest of archivists in the last decade. It has involved discussion of the influence of the emotions of archivists on all aspects of their work. This thesis suggests that archivists give greater attention to emotions as part of the record’s provenance. Provenance is information about the record’s history from its initial inscription through to its placement, description, preservation, and use in archives. Including emotions as part of the record’s provenance can demonstrate the way emotions influence that history. I call this emotions provenance. Traditional archival thinking and practice attempted to silence the emotional aspects of the archivist’s work because of its subjective nature. However, the study of emotions provenance recognizes emotions as an inevitable powerful human experience that influences our mind, body, and behaviour, ultimately shaping the archivist’s understanding of and actions with records, and thus their provenance. The thesis opens with discussion of the call for greater attention to the study of emotions in psychology, history, and archival studies. It then focuses on ways archivists can include emotions provenance in archival descriptions using the early twentieth-century White family photographs at the Archives and Library of the Whyte Museum of the Canadian Rockies in Banff, Alberta. This is based in part on reflection on my own emotional experiences with and historical analysis of these photographs during my internship at the Archives. The thesis maintains that including the archivist’s emotional affect as part of the description of the record’s provenance recognizes the archivist’s role in shaping the record’s history and meaning. Examining emotions provenance helps confirm the archivist’s identity as a co-creator of records whose emotions, among other influences, ultimately also shape historical knowledge and its societal impact.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".