Assisting you to advance with ethics in research: an introduction to ethical governance and application procedures
Bibliographic record
Abstract
Abstract Ethics and ethical behaviour are the fundamental pillars of a civilised society. The focus on ethical behaviour is indispensable in certain fields such as medicine, finance, or law. In fact, ethics gets precedence with anything that would include, affect, transform, or influence upon individuals, communities or any living creatures. Many institutions within Europe have set up their own committees to focus on or approve activities that have ethical impact. In contrast, lesser-developed countries (worldwide) are trying to set up these committees to govern their academia and research. As the first European consortium established to assist academic integrity, European Network for Academic Integrity (ENAI), we felt the importance of guiding those institutions and communities that are trying to conduct research with ethical principles. We have established an ethical advisory working group within ENAI with the aim to promote ethics within curriculum, research and institutional policies. We are constantly researching available data on this subject and committed to help the academia to convey and conduct ethical behaviour. Upon preliminary review and discussion, the group found a disparity in understanding, practice and teaching approaches to ethical applications of research projects among peers. Therefore, this short paper preliminarily aims to critically review the available information on ethics, the history behind establishing ethical principles and its international guidelines to govern research. The paper is based on the workshop conducted in the 5th International conference Plagiarism across Europe and Beyond, in Mykolas Romeris University, Lithuania in 2019. During the workshop, we have detailed a) basic needs of an ethical committee within an institution; b) a typical ethical approval process (with examples from three different universities); and c) the ways to obtain informed consent with some examples. These are summarised in this paper with some example comparisons of ethical approval processes from different universities. We believe this paper will provide guidelines on preparing and training both researchers and research students in appropriately upholding ethical practices through ethical approval processes.
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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 | 0.090 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".