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Record W7027691761

Creating Characters and Costumes for Living History Programs in Late Eighteenth-Century Nova Scotia

2022· dissertation· en· W7027691761 on OpenAlexaboutno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2022
Typedissertation
Languageen
FieldSocial Sciences
TopicHistorical and Cultural Archaeology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsInterpretation (philosophy)Subject (documents)Filter (signal processing)Work (physics)Circumstantial evidencePlan (archaeology)
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.917

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.005
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.047
GPT teacher head0.295
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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