Bibliographic record
Abstract
Watch VIDEO. Publication competency is a fundamental skill for researchers, serving as a vital criterion for attaining a Ph.D. degree. The European Qualifications Framework and Norwegian Qualifications Framework both recognize the importance of these skills. However, institutions differ in their approaches to teaching this skill set, with some neglecting it altogether. The specific skills required for researchers to publish their work extend beyond simply disseminating research appropriately. While the qualifications frameworks offer broad guidelines, various definitions, such as the Vancouver guidelines, the Norwegian NVI guidelines, and Plan S, need consideration. Specifically we have taken publication ethics, understanding impact, copyright and Open Access into consideration. In addition to benefiting Ph.D. students in their own endeavors, publication competency contributes to enhancing information literacy, research principles, and our local institutional knowledge. It establishes a foundation for a more systematic approach to teaching this essential skill. To address this issue, we conducted an analysis of Ph.D. students' competency levels in publication at the University of Agder, through interviews and questionnaires. Our findings align with previous research conducted at other institutions. In our forthcoming paper, we will discuss these findings and their implications for the University Library's approach to disseminating publication competency and creating robust institutional support systems, and suggest a method for increasing publication competency among Ph.D. 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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.012 |
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".