Sources and Information in Academic Research: Avoiding Mistakes in Assessing Sources for Research and during Peer Review
Bibliographic record
Abstract
The credibility of academic publications has come under attack in a variety of circumstances. Newspaper headlines such as “Scientific fraud is rife” and “McGill University finds scientists published ‘falsified’ images” are not helpful in maintaining the credibility of the academic community. Additionally, a cottage industry appears to be growing in websites that specialize in identifying papers which publishers have been forced to retract. The website Retraction Watch has identified one case in which a publisher has retracted 172 papers from one author and may eventually retract 183 papers in total. The website Copy Shake and Paste makes a series of references to PhD dissertations and professorial habilitations which have been questioned or rescinded due to plagiarism. One of the PhDs in question was written by a German Minister of Education and Research.
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 | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
| gpt | Scholarly communication Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.623 | 0.878 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.042 | 0.037 |
| Science and technology studies | 0.018 | 0.019 |
| Scholarly communication | 0.060 | 0.054 |
| Open science | 0.010 | 0.030 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.019 | 0.040 |
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".