Bibliographic record
Abstract
The academic community is under great pressure to publish. This pressure is compounded by high rejection rates at many journals. A more recent trend is for some journals to send invitations directly to researchers inviting them to submit a manuscript to their journals. Many researchers find these invitations annoying and unsure how best to respond to them. We collected electronic invitations to submit a manuscript to a journal between April 1, 2014, and March 31, 2015. We analyzed their content and cross-tabulated them against journals listed in Beall's list of potential predatory journals. During this time period, 311 invitations were received for 204 journals, the majority of which were in Beall's list (n = 244; 79%). The invitations came throughout the calendar year and some journals sent up to six invitations. The majority of journals claimed to provide peer review (n = 179; 57.6%) although no mention was made of expedited review process. Similarly, more than half of the journals claimed to be open access (n = 186; 59.8%). The majority of invitations included an unsubscribe link (n = 187; 60.1%). About half of the invitations came from biomedical journals (n = 179). We discuss strategies researchers and institutions can consider to reduce the number of invitations received and strategies to handle those invitations that make it to the recipients' inbox, thus helping to maintain the credibility and reputation of researchers and institutions.
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 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 | 0.147 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.028 |
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; both teacher heads agree on what is shown here.
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".