Collaborative approaches to designing effective digital image databases for the study of three‐dimensional museum collections
Bibliographic record
Abstract
Purpose This technical paper aims to define the steps necessary to create an effective two‐dimensional image databases representing three‐dimensional museum objects for the purpose of instruction. Design/methodology/approach The participating institutions reviewed six key types of services: finding content, collecting content, accessing content, documentation, accessibility, and access control. The project created, converted, described and transferred digitized images and data records from each partner to the web where they became universally accessible through a single common search interface. Findings The paper finds that collaboration between different institutions creates rich collections, and relationships that benefit the community. Research limitations/implications Capturing elements of three‐dimensional objects in a traditionally two‐dimensional medium provides unique challenges for web delivery. Practical implications Provides learning materials and access to objects that were once locked in storage and rarely exhibited, especially fragile and delicate objects. Also provides an environment for students to learn how to work professionally they would not acquire in the classroom. Originality/value New techniques in digitization were used and experimented with that are not widely used with these type of collections.
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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".