Ten Lessons Learned from Starting a New Scientific Editing Program at a Comprehensive Cancer Center
Bibliographic record
Abstract
Introduction Science editors play an important role in ensuring the integrity of the scientific literature. While journal editors work with authors to improve the clarity and conciseness of manuscripts during the submission, peer review, and publication stages,1 inclusion of professional editors for authors early on during scholarly knowledge production also can be of high value. Specifically, author editors can provide authors with substantial editing support and customized educational resources that have the potential to improve faculty writing skills, boost their productivity, and enhance efficiency at later publication stages. Reports from various medical institutions on the use of such science editors are generally positive.2–6 However, shared experiences with these types of integrated editing–educational interventions targeted at faculty are scarce in the literature. Hence, this topic remains an underreported area of science communications that would benefit from further evaluation and discussion among all professionals involved in the knowledge production pipeline. This article provides a summary of 10 lessons learned from implementing a formal science editing program at Roswell Park Comprehensive Cancer Center in Buffalo, NY—this information was presented earlier in the form of a poster at the 2023 CSE meeting in Toronto, Canada. Roswell Park, founded in 1898, is a National Cancer Institute (NCI)-designated comprehensive cancer center, with approximately 400 faculty who are engaged in basic science and translational, clinical, and population-based research. The editing program, formally called the Scientific Editing and Research Communications Core (SERCC) Resource, was conceptualized following a needs assessment by the Faculty Development Program and Grants Office in […]
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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 | 0.189 | 0.303 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".