How hyped media and misleading editorials can influence impressions about Beall’s lists of “predatory” publications
Bibliographic record
Abstract
Purpose The issue of “predatory” publishing and the scholarly value of journals that claim to operate within an academic framework, namely, by using peer review and editorial quality control, but do not, while attempting to extract open access (OA) or other publication-related fees, is an extremely important topic that affects academics around the globe. Until 2017, global academia relied on two now-defunct Jeffrey Beall “predatory” OA publishing blacklists to select their choice of publishing venue. This paper aims to explore how media has played a role in spinning public impressions about this issue. Design/methodology/approach The authors focus on a 2017New York Timesarticle by Gina Kolata, on a selected number of peer reviewed published papers on the topic of “predatory” publications and on an editorial by the Editor-in-Chief ofREM, a SciELO- and Scopus-indexed OA journal. Findings The Kolata article offers biased, inaccurate and potentially misleading information about the state of “predatory” publishing: it relies heavily on the assumption that the now-defunct Beall blacklists were accurate when in fact they are not; it relies on a paper published in a non-predatory (i.e., non-Beall-listed) non-OA journal that claimed incorrectly the existence of financial rewards by faculty members of a Canadian business school from “predatory” publications; it praised a sting operation that used methods of deception and falsification to achieve its conclusions. The authors show how misleading information by theNew York Timeswas transposed downstream via theREMeditorial. Originality/value Education of academics.
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 | 0.042 | 0.305 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.026 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".