Bibliographic record
Abstract
Guidance on research software preservation is scattered across many libraries, journals, research groups, and organizations' websites. The guidance varies across these websites and does not often address researchers' needs for clear instruction. Researchers/authors are left without a common reference point. The CiteSoftware group's aim is to drive the adoption of a common research software preservation and citation guidance website called Cite.Software for the research community. We are looking for support and guidance from the Research Software Funders Forum. We will leverage the content and development expertise of the Turing Way and The Netherlands eScience Center but also plan on working with key stakeholders involved with research software preservation and citation to develop the resource. The site will be hosted via GitHub (or similar) to foster transparent, collaborative work with the community. In this workshop session, we plan on using the forum as our stepping stone for further guidance and support to progress the project.
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 | 0.011 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.380 | 0.435 |
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".