Bibliographic record
Abstract
In the summer of 2020, C&RL received a request from the ACRL Board of Directors to establish a registered report submission track as a major step to ensure C&RL's high standards of rigorous methods. The request letter was signed by a group of ACRL members, led by Amy Riegelman, who later published an editorial on this topic (Amy Riegelman, 2021), calling C&RL to be more proactive in supporting open research practices. In order to increase C&RL's rigor in supporting and implementing open research practices, it was recognized that both access to research data and transparency of research methods are necessary. From this line of thought, the C&RL Editorial Board, former Editor Wendi Arant Kaspar and Editor Kristen Totleben, have been engaged in an ongoing conversation on the possibility and the journal's capacity to implement a data sharing policy. For the past three years, Editorial Board member Minglu Wang has been researching academic journals' data sharing policies and reaching out to journal editors and editorial board members for consultation. Her efforts culminated in fall 2022 when she, Totleben, and Editorial Board member Adrian Ho conducted a survey (see Appendix) requesting input from colleagues in academic libraries regarding their perceptions of a data sharing policy and what types of data management support they would need or recommend.
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 | 0.417 | 0.681 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.032 | 0.037 |
| Scholarly communication | 0.089 | 0.082 |
| Open science | 0.015 | 0.019 |
| Research integrity | 0.065 | 0.063 |
| Insufficient payload (model declined to judge) | 0.033 | 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 itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier 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".