What's New at <i>JGR‐Oceans</i>? Confronting Bias, Burn Out, and Big Data
Bibliographic record
Abstract
Abstract JGR‐Oceans receives many more submissions from a broader demographic of authors than in the past and burnout among reviewers as well as potential bias among editors is threatening excellence and equity at the journal. To confront these issues, we have implemented some new editorial strategies that are anticipated to provide a fairer and more rewarding peer‐review experience for authors, as well as alleviate pressure on reviewers and deliver high quality science for readers. First, we have recruited a dozen new editors from across the world who better reflect our author demographic and who can make wiser and more inclusive decisions about the running of the journal. Second, we now require that each manuscript clearly communicate new understanding about the ocean before we send it out for review. This simple rubric deflects potentially biased editorial decisions based on author attributes and brings us closer to the original scope of JGR‐Oceans . Third, we are facilitating a culture of collaboration among reviewers and among ourselves, the editors, that brings more balanced decision‐making to reviews and manuscripts and provides authors more feedback. Our aim is to better help authors communicate their science with confidence and clarity. Finally, JGR‐Oceans has always been a multi‐disciplinary journal and we are encouraging more submissions that convey new understanding of biogeochemical processes and human interactions with ocean variability and change.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads 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".