Misleiding herbekeken: een dialoog tussen onderzoeks-ethische ondersteuning en praktijk
Bibliographic record
Abstract
The researcher that will be hired will lead research activities in the domain of research ethics in two different projects. The main focus will be on a project funded by the CELSA Alliance (KU Leuven - Jagiellonian University Medical College) that will focus on the ethical aspects related to research strategies that purposefully mislead research participants. Although methodologies that use deception in their design could potentially increase relevant scientific knowledge in a huge number of areas, there is a lack of understanding about the conditions under which such a methodology is justified and a lack of clarity about what constitutes an appropriate context in which research participants can be deceived. Therefore, the use of deception in research studies has been heavily criticized because of ethical concerns regarding its use; because of the lack of proper informed consent and the fact of misleading the participant to the real purpose of the study. More recently, the development of the General Data Protection Regulation challenges the use of deception even more. This project will allow (1) to systematically identify research studies that used deception; (2) to investigate the experiences of researchers who have used a methodology in which deception was used; and (3) to systematically map research ethics guidelines and recommendations in order to analyse to what extent and the ways in which recommendations deal with the use of deception in research. This project will be jointly supervised with professor Jan Piasecki (Jagiellonian University Medical College) and professor Dieter Baeyens (Faculty of Psychology and Educational Sciences, KU Leuven). Alongside the first project, the researcher will be involved in a bilateral Flanders (Belgium)- Canada project funded through the Research Foundation Flanders on the ethical aspects related to the research use of crowdsourced medical data for biomedical research. Smartphone applications for health are being increasingly used as a platform for collecting and sharing large volumes of crowdsourced personal health data for biomedical research and algorithm training. Consumer genetics products are similarly allowing individuals to have direct access to their own genetic data and to share such data with researchers. Using smartphone and genetic data in these ways presents numerous opportunities to expand biomedical knowledge, though it also raises certain risks. Some of these include risks to personal privacy and risks associated with unclear ethical and legal obligations on the part of app developers and researchers. The research activities will be done through a multi-disciplinary lens and by studying the ethical and policy concerns both from a theoretical and from an empirical perspective through interviews with experts and stakeholders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".