Creating Characters and Costumes for Living History Programs in Late Eighteenth-Century Nova Scotia
Bibliographic record
Abstract
This dissertation delineates the development of a Best Practice Model of Living History Interpretation through greater accuracy in the reconstruction of historical clothing. It specifically focuses on a case study of the Costumed Interpretive Master Plan to be developed for the Nova Scotia Museum, suggesting how the modern understanding of the history of dress and accurate historical clothing can add to the interpreter’s arsenal of tools to help teach history utilizing a broader sensory experience. Through a research-creation project, carried out over several years, I experimented in how one creates clothing using historical methods, and then how those clothing pieces work within a living history environment. This was a form of “living inquiry” or “making as research” in that through the recreation of the garments and then wearing them I could determine how closely the reconstructions worked as clothing instead of costumes. \nThe objectives of this dissertation are two-fold: first is to prove the importance of reconstructed material culture; and, secondly, to examine how experimental archaeological practice can inform and help to better understand how original material culture was made and used in the historical period. I discuss how interpretation developed in the early twentieth century, how re-enactment developed into living history and the important role of accuracy in both the interpreter’s and the audience’s experience of living history. For the characters developed, I consulted with the models I wanted to employ, taking into consideration both the site’s needs, as well as the living history actor’s personality. I began in the Nova Scotia Museum’s collection realizing that expanding my research parameters to include online resources from other museums would be required. To fulfil my research goals, I sought out digitally accessible archives and collections from the north of Scotland to the southern United States and beyond.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".