What We Are Nostalgic For: Place-Making and Social Capital in Local Museums
Bibliographic record
Abstract
What We Are Nostalgic For:Place-Making and Social Capital in Local Museums Megan Kathleen Hull Doctor of Philosophy Faculty of Information University of Toronto 2025 Abstract What roles do local museums play in their communities? This dissertation addresses this question through a case study of the deep connection between the Sooke Region Museum and its community of Sooke, British Columbia. Employing ethnography, autoethnography, and an interdisciplinary framework, this research contributes to an understanding of the importance of local museums to community self-definition and place-making. I make connections between these place-making and self-defining processes by focusing on two key sets of literature – nostalgia and social capital – to make sense of how the museum mediates Sooke’s history and identity tied to resource extraction and its newer identity as a bedroom community. Logging and fishing, the mainstays of Sooke’s economy for decades, have been replaced with tourism and a bedroom community economy and identity, with an influx of new residents drawn by Sooke’s small size, affordable housing, and proximity to Victoria, BC. In Sooke, rapid population change has caused an identity crisis for many, creating tension between old and new ideas about what is important to the community. My research suggests that the local museum has provided a space for community members to develop individual and collective connections to Sooke and build a foundation of social capital with each other and with the museum. Despite being found in communities across Canada, local museums remain understudied and, I argue, undervalued and under-utilized. My research demonstrates that local museums contribute in vital ways to evolving small towns by 1) creating multiple opportunities for nostalgia to form between histories, places and people, and 2) developing local social capital through community-building. Inspired by scholarship that explores nostalgia, I argue that in its most positive and meaningful incarnation – when nostalgia respects the past while acknowledging its historiographic context – it is a powerful way that local museums connect with community members. Leveraging that nostalgia allows local museums to build social capital among community members, and between museums and their communities. By contributing to social capital, nostalgia can help museums facilitate the building of community identity and place-making, which in turn makes communities meaningful and responsive places to live.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.037 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".