Bibliographic record
Abstract
MODERATOR: Julie Vo Senior Editorial Coordinator American Society for Clinical Pharmacology & Therapeutics Alexandria, Virginia SPEAKERS: Christine Melchione Adams Publications Coordinator American Society of Clinical Oncology (ASCO) Alexandria, Virginia Jason Roberts Senior Partner Origin Editorial Ottawa, Ontario, Canada Morgan Sorenson Managing Editor Neurology: Neuroimmunology & Neuroinflamation American Academy of Neurology Minneapolis, Minnesota REPORTER: Meghan McDevitt Managing Editor The Journal of Pediatrics Cincinnati Children’s Hospital Medical Center Cincinnati, Ohio Editorial offices are often asked to provide reports, perhaps annually for an editorial board meeting or ad hoc when requested by an editor. But are these reports being used effectively to influence better editorial decisions? This practical session on editorial office reporting provided attendees with an overview of reporting practices, pitfalls and how to avoid them, and case‐based examples. Jason Roberts, Senior Partner at Origin Editorial, began by discussing the many reasons reports are run and used, such as to monitor progress, set benchmarks, or to anticipate or plan for future developments. However, running a report, obtaining the data required, and analyzing it is not always simple. Many problems exist in editorial office reporting including placing too much meaning on too few data points, overusing a solitary average (rather than a mean and range), and ignoring confounders when interpreting the data. Additionally, a lack of industry standards makes it impossible to compare data across journals. Many editorial offices also experience a lack of continuity between the reports run year-to-year, and thus have no historical context for the data they’re trying to interpret. […]
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 | 0.204 | 0.578 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.030 | 0.020 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.010 | 0.024 |
| Insufficient payload (model declined to judge) | 0.099 | 0.096 |
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".