Learning the language of craft: a publishing workshop for graduate students
Bibliographic record
Abstract
This article outlines a workshop orienting sociology graduate students to overcoming challenges in publishing. Although graduate students are increasingly told to publish, little guidance exists on how to best prepare them for this venture; mentorship scholarship typically assumes the professor–student relationship is the best or most appropriate site of knowledge transmission about publishing. Our workshop is a collective learning experience that can be led either by experienced graduate students or faculty, aimed at developing craft knowledge (techne) about the publishing process. Participants in our workshops reported (1) that they were a site of affect normalization, helping them to understand they were not alone in fearing anonymous peer review and receiving harsh critiques of their work from peer reviews; and (2) appreciated concrete case studies of navigating the peer review process. We encourage other departments to use or modify this workshop to normalize the publishing process for graduate students.
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".