On the SEAJ Ethos: Mentorship and Peer Review
Bibliographic record
Abstract
These editorial reflections revisit SEAJ's mentoring ethos, outlining how we embrace it as joint editors. To this end, we mobilize the caretaker analogy, hoping to convey the nurturing position we strive to adopt in our role, which involves valuing each other's intellectual contributions and promoting social and climate justice through the pursuit of transformative quality research and scholarship while proactively responding to issues of inequity and injustice using the levers we have at our disposal. In light of the recent resurfacing of wider concerns over peer review process in scientific publishing, we continue these reflections through considerations of what embracing the SEAJ ethos implies in the peer review process, humbly hoping to recognize and respect the valuable contribution reviewers make to the journal while also developing a community-based perspective on peer review (Souder, 2011).
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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 | 0.136 | 0.340 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.043 |
| Scholarly communication | 0.051 | 0.032 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.017 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".