Bibliographic record
Abstract
In conjunction with the International 2010 Open Access Week (October Oct. 18-24th,), the BC Research Libraries Group invited G. Sayeed Choudhury, Associate Dean for Library Digital Programs and Hodson Director of the Digital Research and Curation Center at the Sheridan Libraries of Johns Hopkins University, to speak on the Case for Open Data and eScience – Establishing a University Data Management Program at Johns Hopkins. Sayeed Choudhury discussed John Hopkins University (JHU) work developing a university data management program and a service model to support data curation as part of an evolving cyberinfrastructure featuring open, modular components in support of JHU faculty associated with community-wide eScience projects. In addition to developing a technological framework for data conservancy at JHU, they are also developing new roles and relationships between the library and the academic community, most notably through the development of “data scientists” or “data humanists.” Within these developments, Choudhury concluded that institutional repositories is the first step in a longer journey towards data conservation and that for institutional efforts to be successful, they must be integrated into a larger landscape of repositories that serve a distributed and diverse academic community.
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 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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".