How does correlation structure differ between real and fabricated data-sets?
Bibliographic record
Abstract
BACKGROUND: Misconduct in medical research has been the subject of many papers in recent years. Among different types of misconduct, data fabrication might be considered as one of the most severe cases. There have been some arguments that correlation coefficients in fabricated data-sets are usually greater than that found in real data-sets. We aim to study the differences between real and fabricated data-sets in term of the association between two variables. METHOD: Three examples are presented where outcomes from made up (fabricated) data-sets are compared with the results from three real data-sets and with appropriate simulated data-sets. Data-sets were made up by faculty members in three universities. The first two examples are devoted to the correlation structures between continuous variables in two different settings: first, when there is high correlation coefficient between variables, second, when the variables are not correlated. In the third example the differences between real data-set and fabricated data-sets are studied using the independent t-test for comparison between two means. RESULTS: In general, higher correlation coefficients are seen in made up data-sets compared to the real data-sets. This occurs even when the participants are aware that the correlation coefficient for the corresponding real data-set is zero. The findings from the third example, a comparison between means in two groups, shows that many people tend to make up data with less or no differences between groups even when they know how and to what extent the groups are different. CONCLUSION: This study indicates that high correlation coefficients can be considered as a leading sign of data fabrication; as more than 40% of the participants generated variables with correlation coefficients greater than 0.70. However, when inspecting for the differences between means in different groups, the same rule may not be applicable as we observed smaller differences between groups in made up compared to the real data-set. We also showed that inspecting the scatter-plot of two variables can be considered as a useful tool for uncovering fabricated data.
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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 | 0.124 | 0.459 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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; 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".