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Record W6904927205 · doi:10.14288/1.0448705

Sustaining community archives through social innovation model : an exploratory study

2025· article· en· W6904927205 on OpenAlexaboutno aff

Bibliographic record

VenuecIRcle (University of British Columbia) · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicDigital and Traditional Archives Management
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilitySocial innovationExploratory researchDesign thinkingRevenueSocial sustainabilityIndependence (probability theory)Business modelExhibition

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.045
GPT teacher head0.212
Teacher spread0.168 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations1
Published2025
Admission routes1
Has abstractyes

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