The benefits to climate science of including early-career scientists as reviewers
Bibliographic record
Abstract
Abstract. Early-career scientists (ECSs) are a large part of the workforce in science. While they produce new scientific knowledge that they share in publications, they are rarely invited to participate in the peer-review process. Barriers to the participation of ECSs as peer reviewers include, among other things, their lack of visibility to editors, inexperience in the review process and lack of confidence in their scientific knowledge. Participation of ECSs in group reviews, e.g. for regional or global assessment reports, provides an opportunity for ECSs to advance their skill set and to contribute to policy-relevant products. Here, we present the outcomes of a group peer review of the First Order Draft of the Intergovernmental Panel on Climate Change (IPCC) Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC). Overall, PhD students spent more time on the review than those further advanced in their careers and provided a similar proportion of substantive comments. After the review, participants reported feeling more confident in their skills, and 86 % were interested in reviewing individually. By soliciting and including ECSs in the peer-review process, the scientific community would not only reduce the burden carried by more established scientists but also permit their successors to develop important professional skills relevant to advancing climate science and influencing policy.
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 | 0.327 | 0.664 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 0.017 |
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".