Bestiary of Questionable Research Practices in Psychology
Bibliographic record
Abstract
Questionable research practices (QRPs) pose a significant threat to the integrity and credibility of scientific research. However, historically, they remain ill-defined and a comprehensive list of QRPs is lacking. The article addresses this concern by defining, collecting, and categorizing QRPs using an expert consensus method. Collaborators of the study agreed on the following definition: “Questionable research practices (QRPs) are ways of producing, maintaining, sharing, analyzing, or interpreting data that are likely to produce misleading conclusions, typically in the interest of the researcher. QRPs are not normally considered to include research practices that are prohibited or proscribed in the researcher’s field (e.g., fraud, research misconduct). Neither do they include random/non-motivated researcher error (e.g., accidental data loss).” Drawing from both iterative discussions and existing literature, we collected, defined and categorized 40 QRPs. We also considered attributes such as potential harms, detectability, clues, and remedies for each QRP. The results suggest that QRPs are pervasive and versatile, and have the potential to undermine all stages of the scientific enterprise. This work contributes to the maintenance of research integrity, transparency, and reliability by raising awareness for and improving the understanding of QRPs.
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 | 0.319 | 0.473 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.021 | 0.103 |
| Scholarly communication | 0.037 | 0.032 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".