Do they practice what they preach? The presence of problematic citations in business ethics research
Bibliographic record
Abstract
Purpose In scholarly publications, citations play an essential epistemic role in creating and disseminating knowledge. Conversely, the use of problematic citations impedes the growth of knowledge, contaminates the knowledge base and disserves science. This study investigates the presence of problematic citations in the works of business ethics scholars. Design/methodology/approach The authors investigated two types of problematic citations: inaccurate citations and plagiarized citations. For this, 1,200 randomly selected citations from three leading business ethics journals were assessed based on: (1) referenced journal errors, (2) article title errors and (3) author name errors. Other papers that replicated the same title errors were identified. Findings Of the citations in the examined business ethics journals, 21.42% have at least one error. Of particular concern are the citation errors in article titles, where 3.75% of examined citations have minor errors and another 3.75% display major errors – 7.5% in total. Two-thirds of minor and major title errors were repeatedly replicated in previous and ensuing publications, which confirms the presence of citation plagiarism. An average article published in a business ethics journal contains at least three plagiarized citations. Even though business ethics fares well compared to other disciplines, a situation where every fifth citation is problematic is unacceptable. Practical implications Business ethics scholars are not immune to the use of problematic citations, and it is unlikely that attempting to improve researchers' awareness of the unethicality of this behavior will bring a desirable outcome. Originality/value Identifying that problematic citations exist in the business ethics literature is novel because it is expected that these researchers would not condone this practice.
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
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 | 0.078 | 0.487 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".