Ethical Guidelines for Conducting Experiments and Writing Scientific Reports in Psychology
Bibliographic record
Abstract
Recently, many cases of scientific malpractice have been reported, and their severity has resulted in the dismissal of those involved, the rescission of academic degrees, expulsion from academic organizations, and even prison sentences. Because it is essential to provide ethical training to people involved in scientific research, the objective of this paper is to describe the ethical guidelines that everyone who conducts experiments in psychology must observe, especially when human participants are involved. These guidelines are also applicable to authors of scientific papers. Our goal is to contribute to ensuring the ethical performance of scientific work. Also, in an effort to eradicate scientific malpractice, we propose implementing a three-pronged strategy: first, working with academic institutions (universities, research centers, etc.) to provide ongoing training in the ethical aspects of the discipline in question to all personnel involved in scientific work (researchers, technicians, professors, students); second, designing strategies for constant, close supervision to guarantee that all scientific activities adhere to the applicable ethical standards; and, third, defining mechanisms to establish and then apply sanctions in the event of scientific malpractice, including the creation of organs entrusted with organizing and implementing these activities.
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 | 0.273 | 0.402 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.026 | 0.034 |
| Insufficient payload (model declined to judge) | 0.016 | 0.018 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".