Understanding eScience: Reflections on a Houston Symposium
Bibliographic record
Abstract
EScience is a data-driven research concept that encompasses the creation of vast data sets on a broad interdisciplinary scale. It is gaining momentum as a venue for librarians to collaborate with researchers and scientists like never before. The data curation necessary for eScience activities will provide librarians with a new platform for demonstrating their expertise in data retrieval, collection, and storage. This paper provides a reflection on the Houston eScience symposium and how it culminated in the creation of the Library’s first eScience Task Force and the Library’s eScience Portal, “Understanding eScience.” As outlined in the paper, this portal benefits TMC researchers and librarians with current information on local and national eScience news, education and events, as well as resource links for creating data management plans mandated by federal research-funding agencies.
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 | 0.062 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.022 | 0.030 |
| Scholarly communication | 0.034 | 0.048 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.011 | 0.027 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".