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
Data collected from 50 institutional repositories (IR) on various platforms and from around the world will be analyzed for this presentation to demonstrate aggregate IR performance, use, and the visibility of content. The Repository Analytics & Metrics Portal (RAMP) is a free web service developed in 2017 with funding from the Institute of Museum and Library Services. The dataset collected by RAMP currently exceeds 300 million rows and it is the only open aggregate data available to evaluate the visibility and use of IR content, diagnose deficiencies with performance, align content with user needs, and optimize metadata for maximum click-through ratios, among myriad other potential uses. This presentation will address several potential research questions that could help improve IR performance and demonstrate the IR value proposition. Methods for extending the RAMP dataset’s analytic potential through augmentation with complementary, publicly available datasets will be described. The presentation will encourage audience members to register their own repositories with RAMP and/or to consider additional ways to analyze the dataset.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.005 | 0.060 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.043 | 0.023 |
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