Bibliographic record
Abstract
This paper proposes major changes in how research misconduct cases should be managed in the USA and Canada. Specifically, I advocate for centralized oversight that completely removes research institutions from this role in order to: mitigate institutional conflicts of interest, standardize definitions of research misconduct, better preserve confidentiality of complainants (those alleging misconduct), ensure that cases are not screened for rejection, mobilize a review panel of experts who are free of conflicts of interest, avoid inappropriate collective punishment of institutions, and ultimately save resources as compared to current decentralized systems. Two cases which this author, as complainant, alleged research misconduct (in the USA and Canada) demonstrate clearly how far institutional Research Integrity Officers can go to prevent an impartial expert review. Given that our institutions and scientific community rightly have zero tolerance for research misconduct, the current decentralized practice should be a grave concern to those who hope to trust in proper oversight. A discussion follows, including comments on new directives for 2025 from the US Office of Research Integrity and the implications of high-profile cases. I conclude with details as to how cases might be brought to justice under the proposed centralized process.
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: Evaluation · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Not applicable | low |
| gpt | MetaresearchResearch integrity Domain: Evaluation · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Other design | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".