Sustaining community archives through social innovation model : an exploratory study
Bibliographic record
Abstract
This thesis examines the sustainability challenges faced by community archives and proposes social innovation theories and design thinking as a solution. Through a comprehensive literature review and reflection on the Community Archives, Collections and Heritage Exhibition (CACHE) project with Vancouver's Wongs' Benevolent Association, this thesis identifies limitations in existing sustainability approaches and shows how social innovation theories, implemented through design thinking, can address root causes and provide sustainable revenue streams while maintaining community archives' independence and autonomy. The study critiques current archival practices and funding models, advocating for human-centric, tailored solutions that generate both social impact and financial sustainability. Drawing from my practical experience on the CACHE project, this thesis offers a methodology of design thinking on adopting social innovation theories as well as a business model framework that provides potential revenue streams while maintaining independence and autonomy. The findings suggest that social innovation can not only enable community archives to achieve archival sustainability but also catalyze systemic change in archival practices, challenging institutional paradigms while empowering communities to preserve and advocate on their own terms.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".