Reflections on Creating a Student-Run Journal: A Duo-ethnography
Bibliographic record
Abstract
Literature regarding graduate student training suggests that graduate students struggle to become involved in academic publishing. Once involved in the publication process, however, graduate students are able to transform their learning, as well as develop knowledge and skills for their future careers. To help further foster student involvement in the publication process at the Werklund School of Education (WSE), the University of Calgary, seven graduate students from educational research and psychology decided to launch a student-run, peer-reviewed research journal called Emerging Perspectives: Interdisciplinary Graduate Research in Education and Psychology (EPIGREP). Using Norris and Sawyer’s (2012) duo-ethnographic approach, this article focused on the editorial team members’ shared reflections and experiences as they answered questions regarding the identified gaps that EPIGREP would fill in terms of graduate student training, the challenges and barriers faced during the inaugural year, and the ways in which participation in the journal could empower journal users to engage in the publication process. Finally we noted implications and future directions regarding establishing EPIGREP as a graduate student initiative to foster research participation.
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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 | 0.043 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.024 | 0.016 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".