From Crud to Cream: Imagining a Rich Scholarly Repository Interface
Bibliographic record
Abstract
This article addresses the design of a dynamic repository interface to support numerous scholarly activities. Starting with the four fundamental functions associated with persistent storage — create, read, update, and delete (CRUD) — we tested, as an organizing rubric for the interface, the acronym CREAM: Create (represent, illustrate); Read (sample, read); Enhance (refer, annotate, process); Analyze (search, select, visualize, mine, cluster); and Manage (track, label, transform). Based on a card-sorting exercise conducted with researchers, we conclude that a slightly modified rubric of CREAMS offers a useful starting point that emphasizes the enriched functionality a scholarly repository or similarly complex digital environment requires, as well as the immense challenge of designing conceptually clear interfaces, even for a relatively homogenous community of researchers.
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How this classification was reachedexpand
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 | 0.023 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.026 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".