What Is the Role of Graduate Student Journals in the Publish-or-Perish Academy? Three Lessons from Three Editors-in-Chief
Bibliographic record
Abstract
To be prepared to face the “publish-or-perish” reality of contemporary academia, early career scholars must develop capacity and confidence. While the publication practices of International Relations have received increasing attention in the last 20 years, concern remains around the preparedness of graduate students to participate confidently and competently in the publication process. As three former Editors-in-Chief of a graduate student journal, we suggest that student-run journals can play an important role in professionalization during graduate school. We then reflect on our journal’s context as well as on reforms initiated to improve the policies and practices during our editorial tenure. Bringing our experiences to bear on previous findings in the literature, we outline three key lessons that can help support successful journals at other institutions. First, given the high turnover rate, starting early is key to maintain early enthusiasm and flatten intensity spikes. Second, editors must remain mindful of what we call the ‘workload paradox’—or how the comparatively low workload of some graduate journals can make it harder to manage an editorial team. Finally, we argue that graduate student journals should be understood as places of learning and primarily valued as professionalization and pedagogical spaces.
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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 | 0.079 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.059 | 0.024 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".