Addressing challenges and finding solutions: navigating the informal curriculum of publication training within higher education
Bibliographic record
Abstract
The peer-review process used in most academic journals is critical for curating evidence-based knowledge. Although many graduate programmes expect students to engage in the research process, these programmes often do not mandate formal training for the publication process. With few studies examining the graduate student experience in the publication process, the current study sought to help address this gap. Specifically, we used a reflexive thematic analysis approach to look at 18, hour-long, semi-structured interviews with Education graduate students at the master’s and doctoral level. Using a revised version of Kolb’s experiential learning theory to contextualise the results, we highlight three main areas related to participants’ experiences engaging in the publication process: a) experiences and motivations, b) challenges, and c) pathways to publishing. Based on these findings, we offer implications for higher education to support graduate students’ readiness to engage in the publication process as future scholars.
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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 | 0.158 | 0.237 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.020 |
| Scholarly communication | 0.027 | 0.020 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".