Bibliographic record
Abstract
The Repository Rodeo returns for another round of questions and answers! This popular panel, featured since Open Repositories 2016 in Dublin, offers a broad overview of the main repository platforms at Open Repositories and provides an opportunity for spirited discussion amongst panelists and attendees. Join community representatives from Dataverse, DSpace, EPrints, Fedora, Haplo, Invenio, Islandora, and Samvera as we briefly explain what each of our repositories actually does. We'll also talk about the directions of our respective technical and community developments and related to the conference theme of "Building Trust Together", we’ll discuss the role of our repositories, and repository communities, in enabling trust, integration, collaboration, and sharing. This panel will be a great opportunity for newcomers to Open Repositories to get a crash course on the major repository options and meet representatives from each of their communities. After a brief presentation from each representative, we'll open the session up for questions from the audience.
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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.000 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".