Visualizing Collaborative Filtering in Digital 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
The NEAR (navigating exhibitions, annotations and resources) panel is a method of managing digital collections and user preferences through collaborative filtering and graphically revealing implicit data relations such as sharing, reference and similarity. It is implemented on AldrVIldrRE, an online multimedia repository. AldrVIldrRE supports semi-structured collections (exhibitions) which containing various resources and annotations. Its users are encouraged to contribute, share, annotate and interpret resources. Similar to the act of adding items into shopping carts in the e-commence applications, a user's activities of searching, organizing and interpreting data in AldrVIldrRE are considered as evidence of user's preferences. The design process of NEAR was guided by several principles from the visualization literature. It implements new navigation and communication approaches that support discovery of relations. Having tested NEAR with several users, we further analyze the design, report the evaluation and consider its use in other applications.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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