Speeding up with higher quality: Introducing the new Campbell Editorial Advisory Board
Bibliographic record
Abstract
The Campbell collaboration is the preeminent source for high quality evidence synthesis in the social sectors. Over the past 5 years that I have been editor in chief, we have doubled our publishing of systematic reviews, evidence and gap maps and methods research papers. We have also doubled our team of editors, methods editors and information specialists. However, we need to grow the community of content reviewers who provide external feedback on the domain or content of our articles. As part of our new strategy to make evidence synthesis faster and more useful (https://www.campbellcollaboration.org/news-and-events/news/stepping-up-evidence-synthesis.html), this month, we are delighted to launch a new Editorial Advisory Board of peer referees, who are committed to contributing three to four referee assessments per year. These referees are now named on our Editorial Advisory Board page at the following link (https://onlinelibrary.wiley.com/page/journal/18911803/homepage/editorial-board). We have reached out to our networks to seek geographic and disciplinary diversity in this board. We see this peer referee board as a means to build the Campbell community, inviting new participants as well as those who are already members to continue their contributions beyond authorship. In future, we see the new Editorial Advisory Board as a pathway for people to join our other editorial activities as editors, methods editors or information specialists. As a research-based organization, we will monitor the effectiveness of this new Editorial Advisory Board in reducing our editorial turnaround times. These turnaround times will be publicly available next year through our journal site at Wiley online library. If you would like to get involved in Campbell in this way, we outline our expectations for the role of these referees, available here (https://onlinelibrary.wiley.com/page/journal/18911803/homepage/referees). We invite you to get in touch by writing to [email protected]. Referees are expected to have substantive content expertise in one or more social science sectors relevant to Campbell, and to be willing to review three to four Campbell articles per year. We offer recognition through Publons for peer referee contributions. For funded reviews, we can compensate peer referees for their time. We plan to launch an early career researcher network in the next 3 months, to which peer referees will be invited. Please join us!
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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 | 0.043 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 0.012 |
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".