Analogous Analogues: Digital Twins and Hardware Tracking in GLAM Collections
Bibliographic record
Abstract
Galleries, Libraries, Archives, and Museums (GLAMs) are host to cultural treasures and historic records but face inherent challenges maintaining accessibility and traceability in their legacy collections. Rolling COVID-19 lockdowns over the past three years (2020-2023) have limited access to primary materials while user expectation of digital access to collections has grown. With renewed digital access, however, comes new challenges in authentication and provenance tracking: collection digitization and monitoring of cultural artefacts introduces new lines of work for institutions already constrained by budgets and staffing. Building upon our previous exploration of this topic, “NFTs: Tulip Mania or Digital Renaissance?”, we present a design solution for tracking and monitoring GLAM collection objects via a hardware controller with Trusted Execution Environment (TEE) that interfaces with a trusted and flexible digital twin ledger architecture, selected from our analysis of database and private ledger technologies. We conclude by outlining the physical threat model for this design: future work will expand this model to include digital (cyber) threats to GLAM collection objects and investigate credentialed queries.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".