Bibliographic record
Abstract
The purpose of this paper is to explore the possibilities of virtual exhibitions (VEs) for GLAM institutions. While VEs continue to be secondary to physical exhibitions, an effective VE uses technology to engage viewers and present opportunities for interactivity to support further learning and discovery of collection materials. Cultural heritage organizations can use VEs to make the “digital version of a cultural artefact accessible even when the physical access is restricted” and leads GLAM institutions and LIS scholars to reflect on how “users receive and interact with information in a virtual world” (Caggianese et al., 2018, p.625). With the aid of easily accessible additional information, this modern form of presentation may support a deeper level of understanding than a user can experience by viewing a traditional physical exhibition, and may enhance their overall viewing experience. Despite the excitement and opportunities afforded by VR, exhibitions remain accessible for visitors without VR equipment via browsers or web-page based exhibitions. As the “virtual exhibition is a concept that has acquired new meanings along with the evolution of modern information and communication technologies,” we look forward to seeing how GLAM institutions continue to shape the user experience (Ciurea & Filip, 2016, p.28). Cultural organizations will continue to develop and combine their partnerships, financial and staff resources, content, and visitor interests to build more VE structures that fit both their collections and their community.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.079 | 0.016 |
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".