A Mismatched Group of Items That I Would Not Find Particularly Interesting: Challenges and Opportunities with Digital Exhibits and Collections Labels
Bibliographic record
Abstract
Objective – The authors sought to identify link language that is user-friendly and sufficiently disambiguates between a digital collection and digital exhibit platform for users from a R1 institution, or a university with high research activity and doctoral programs as classified in the Carnegie Classification of Institutions of Higher Education. Methods – The authors distributed two online surveys using a modified open card sort and reverse-category test via university electronic mailing lists to undergraduate and graduate students to learn what language they would use to identify groups of items and to test their understanding of link labels that point to digitized cultural heritage items. Results – Our study uncovered that the link terms utilized by cultural heritage institutions are not uniformly understood by our users. Terms that are frequently used interchangeably (i.e., Digital Collections, Digital Project, and Digital Exhibit) can be too generic to be meaningful for different user groups. Conclusion – Because the link terms utilized by cultural heritage institutions were not uniformly understood by our users, the most user-friendly way to link to these resources is to use the term we—librarians, curators, and archivists—think is most accurate as the link text based on our professional knowledge and provide a brief description of what each site contains in order to provide necessary context.
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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 | 0.015 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".