Crafting Identity through Folk: The Construction of National Identity within Serbian Diasporic Institutions and Homes
Bibliographic record
Abstract
This thesis investigates how embroideries and textiles that are collected and exhibited within Serbian Canadian homes and museums serve to develop a sense of national identity. By conducting two case studies, involving The Serbian Heritage Museum, created by Serbian immigrants in Windsor, Ontario, and the Jancovic family collection, an intergenerational family collection in Montreal, Quebec, this research will analyze their archived textiles, curatorial texts, and interviews. Comparing these collections to the work of The Republic of Serbia Ministry of Culture and Information’s work with UNESCO, which aims to define a sense of national identity \nto combat the perceived risk of obsolescence as a result of the growing impact of globalization, reveals how immigrant communities develop their own identities in comparison to governmental bodies. In exploring these three forms of identity building within Serbian communities, one can observe how heritage crafts gradually come to function as commodities rather than artifacts within certain institutional settings, while also increasingly catering to the nostalgia of a pre-industrial pastoral life. Through this threefold examination of Serbian national identity, this thesis will analyze the malleable affiliations of tradition and authenticity, as well as the shifting \nroles of tool, artifact, and commodity placed upon folk art in identity building contexts.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".