The role of “unpublished” research in the scholarly communication of scientists: Digital preprints and bioinformation databases. Sponsored by SIG STI, SIG BIO, SIG PUB
Bibliographic record
Abstract
Abstract The advent of the Internet has stimulated the emergence of novel methods of scientific discourse that have the potential to alter traditional communication channels. On a larger scale, new digital information resources have the capacity to change both the way scientists work and the core of scientific knowledge. Historically the hallmark of scientific communication has been the publication of research findings in a peer‐reviewed journal. On its route to the journal, the research may be communicated in many other forms, including conference proceedings, technical reports, and preprints. Recently, models of scientific communication have been updated to include electronic submission of manuscripts, virtual conferences, e‐mail, and online journal publication. In fact, electronic preprints have become a primary mode of information dissemination in physics and astronomy. In contrast, biomedical scientists are reluctant to accept the electronic preprint as a viable mode for their scholarly communication due to the lack of peer‐review and the uncertain permanence of electronic storage. These same scientists, however, are willing to share their DNA and protein sequence data by depositing it in a variety of the more than 200 publicly available web‐based databases including GenBank and the EMBL Nucleotide Sequence Database. The panelists in this session will discuss the current and potential impact of these large, dynamic, yet not peer‐reviewed, information warehouses on the scholarly communication of scientific researchers. Their insights will provide a fresh prospective on the ways scientists in a range of disciplines are coping with the 21st century digital information flood.
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 | 0.147 | 0.228 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.034 | 0.020 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".