Correction and retraction practices in library and information science journals
Bibliographic record
Abstract
Retraction of scholarly publications ensures that unqualified knowledge is purged from the scientific community. However, there appears to be little understanding about how this is practiced among library and information science (LIS) journals. Hence, this study investigated the correction and retraction practices of LIS journals. Journals included in the Web of Science’s information science and library science subject category were selected for the study and the characteristics of the articles corrected or retracted in those journals between 1996 and 2016 were examined. Findings show that there were 517 corrections and five retractions in LIS journals during the period. Most of the corrections made to articles in LIS journals were minor while the reasons for article retraction included plagiarism, duplication, irreproducible results and methodological errors. Our findings also reveal that on average it took about 587 days for an article to be retracted while some of the retracted articles continued to be cited after retraction. The study concluded that the average number of errors per correction was lower than what had been observed in medical journals while some of the retracted articles continued to receive positive post-retraction citations. It also recommended the inclusion of a check on the validity of literature cited by authors at the review stage as part of the quality control mechanism by publishers of LIS journals.
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 integrityScholarly communication Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | MetaresearchResearch integrity Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | 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 | 0.081 | 0.460 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".