Shaking it up: Embracing new methods for publishing, finding, discussing, and measuring our research output
Bibliographic record
Abstract
Abstract The scholarly communication ecosystem is changing. Scholars produce and publish a wider range of products than ever before, and scholars and others increasingly interact with these diverse products in new ways within the online ecosystem. The widespread availability of research products and interaction paths is informing new methods for finding, discussing, measuring, and rewarding diverse types of research output. Some research fields are adopting these new methods faster than supporting tools, processes, and policies can keep up. In other fields the changes are happening very slowly – perhaps at the expense of accelerated progress and impact. We have assembled a panel of information science researchers who both study and implement many of these new ways of doing research. Together with attendees of the session (you!), we will consider several new methods of scholarly communication, highlight some of their strengths and drawbacks, and discuss how they play out today in the field of information science. The session will itself follow a nontraditional format. We will begin with an out‐of‐your‐seat and into‐the‐action icebreaker to capture audience‐driven opinions of several fundamental issues behind these changes. Panelists will then briefly highlight several of the new approaches, including motivation, evidence of benefit (or lack thereof), and how the new method is or could make a difference in information science research. We encourage audience members to document their thoughts on these points during the panelist presentations. Audience notes will be summarized in a poster within the Interactive Showcase later in the conference. We hope this panel will inspire conversation about the ways these new approaches may impact how we study scholarly communication, as well as how we participate in scholarly communication ourselves.
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 | 0.482 | 0.561 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.056 | 0.061 |
| Open science | 0.007 | 0.032 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 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; 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".