Analogous Analogues: Digital Twins and Hardware Tracking in GLAM Collections
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it