Defining, Constructing, and Communicating Heritage at a "Living History" Museum: The Case of the Ukrainian Cultural Heritage Village
Bibliographic record
Abstract
The Ukrainian Cultural Heritage Village, an open‐air, living history museum, provides a case study of how heritage is defined and presented. Drawing on David Lowenthal’s conception as heritage as a social construction and Diane Barthel’s idea of “symbolic bankers”, this paper explores how the Village has defined heritage and who has been involved in its definition. This paper will argue that the Village uses heritage to promote the cultural identity of the Ukrainian community while simultaneously strengthening Albertan pride and ‘nationalism’ through recognizing diversity and multiculturalism, but excludes the heritages of First Nations peoples and the other settler nations. The paper then evaluates the effectiveness of the Village’s attempts to portray history and communicate heritage considering the first‐person method of interpretation used and the involvement of the Alberta Government. The paper finds that the limitations of first‐person interpretation and the economic goals of the Alberta Government have led the Village to a position where it risks the trivialization of Ukrainian cultural meanings and the simplification and sanitization of Alberta’s historical narrative.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.023 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".