Reply to the letter to editor titled “mentoring program: bridging gaps for international authors”
Bibliographic record
Abstract
To the Editor, We appreciate the thoughtful comments on our article, “Journal editing and peer review in the international setting,” published in the November issue of Radiology Advances.1 As highlighted by Hygino da Cruz et al. in their letter, the Radiographics international team has created a more inclusive academic radiology landscape by mentoring and supporting international authors in publishing their educational exhibits as full journal articles, moreover as first authors—a privilege not easily accessible to all. Our article, however, takes a somewhat different focus, aiming to guide international academics on the rewarding path of peer reviewing and editing, empowering them to play an active role in shaping the future of science.2 We identify common challenges in this journey and provide practical, actionable advice to overcome them. Our efforts to facilitate a broader demographic pool for scientific peer review and editing are especially critical given the persistent barriers to participation, particularly for individuals from diverse geographic and socioeconomic backgrounds, as noted by Hygino da Cruz et al. As highlighted by both articles, RSNA and its journals have implemented targeted initiatives to advance their mission of transforming education, publishing groundbreaking research, and providing outstanding academic support to imaging specialists worldwide.1,2 To achieve these objectives, they have established various roles to optimize the publishing process while championing diversity, equity, and inclusion. Through these efforts, RSNA has made substantial strides in bridging gaps across the academic spectrum, encompassing international authors, peer reviewers, and editors. In conclusion, the initiatives by RSNA journals are undoubtedly positively transforming the academic radiology landscape. Programs like the RSNA journals Trainee Editorial Boards and the Radiographics international team initiatives pave the way for a more inclusive scholarly community. The future of academic radiology is indeed bright! None. None declared. N.N.P.: Deputy Editor for Radiology Advances. V.A.F.: Associate Editor for Radiology Advances. H.A.: Associate Editor for Radiology Advances.
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 | 0.007 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.067 | 0.054 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".