How to set the agenda for training in responsible conduct of research using the target audience as a narrative guide
Bibliographic record
Abstract
There is a growing interest in training on responsible conduct of research (RCR). The availability and range of such training materials, for a diversity of audiences and addressing a diversity of RCR related topics, is growing too. This is of great help to all who are looking for materials to set up their RCR training, but how can one select what topics to include within the often-limited time available for training? In this paper we propose a step-by-step approach to set the agenda for RCR training, using the target audience as a narrative guide. The process consists of six steps: (1) mapping the needs and translating them into learning objectives; (2) prioritizing, selecting, and combining objectives and themes; (3) demarcation of the training; (4) completion of the program; (5) development or adaptation of training materials; and (6) pilot and evaluation. The development process of a training program for researchers in Dutch Universities of Applied Sciences is used as an example to illustrate the step-by-step process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".