Invitations received from potential predatory publishers and fraudulent conferences: a 12-month early-career researcher experience
Bibliographic record
Abstract
PURPOSE OF THE STUDY: This study aims to describe all unsolicited electronic invitations received from potential predatory publishers or fraudulent conferences over a 12-month period following the first publication as a corresponding author of a junior academician. STUDY DESIGN: Unsolicited invitations received at an institutional email address and perceived to be sent by predatory publishers or fraudulent conferences were collected. RESULTS: A total of 502 invitations were included of which 177 (35.3%) had subject matter relevant to the recipient's research interests and previous work. Two hundred and thirty-seven were invitations to publish a manuscript. Few disclosed the publication fees (32, 13.5%) but they frequently reported accepting all types of manuscripts (167, 70.5%) or emphasised on a deadline to submit (165, 69.6%). Invitations came from 39 publishers (range 1 to 87 invitations per publisher). Two hundred and ten invitations from a potential fraudulent conference were received. These meetings were held in Europe (97, 46.2%), North America (65, 31.0%), Asia (20.4%) or other continents (5, 2.4%) and came from 18 meeting organisation groups (range 1 to 137 invitations per organisation). Becoming an editorial board member (30), the editor-in-chief (1), a guest editor for journal special issue (6) and write a book chapter (11) were some of the roles offered in the other invitations included while no invitation to review a manuscript was received. CONCLUSIONS: Young researchers are commonly exposed to predatory publishers and fraudulent conferences following a single publication as a corresponding author. Academic institutions worldwide need to educate and inform young researchers of this emerging problem.
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 | 0.018 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".