Research integrity requires to be aware of good and questionable research practices
Bibliographic record
Abstract
P ublications are at the epicenter of the academic system, be it for hiring, career advancement, or funding. The probability of getting a manuscript published in a scientific journal often depends on whether the results are significant, novel, or even "glamorous". Yet, this favoritism is difficult to justify from a scientific viewpoint. The purpose of science is to incrementally build knowledge. Knowing that a variable influences another variable is as important as knowing that this effect does not exist or is unclear. Moreover, the overemphasis on the findings of an article creates an incentive to submit results that are more likely to be accepted for publication, even if those results do not accurately reflect reality. Therefore, the nature of the results should not be considered when deciding whether a manuscript should be accepted or rejected. Such decisions would contribute to making questionable research practices (QRPs) irrelevant.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchResearch integrity Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | MetaresearchResearch integrity Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.701 | 0.876 |
| Meta-epidemiology (narrow) | 0.004 | 0.009 |
| Meta-epidemiology (broad) | 0.016 | 0.006 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.008 | 0.068 |
| Scholarly communication | 0.033 | 0.029 |
| Open science | 0.012 | 0.013 |
| Research integrity | 0.058 | 0.047 |
| Insufficient payload (model declined to judge) | 0.011 | 0.020 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".