A psychological perspective towards understanding the objective and subjective gray zones in predatory publishing
Bibliographic record
Abstract
Abstract A continued lack of clarity persists because academics, policymakers, and other interested parties are unable to clearly define what is a “predatory” journal or publisher, and a potentially wide gray zone exists there. In this perspective, we argue that journals should be evaluated on a continuum, and not just in two shades, black and white. Since evaluations about what might constitute “predatory” are made by humans, the psychological decision-making system that determines them may induce biases. Considering such human psychological characteristics might shed light on the deterministic criteria that have been used, and continue to be used, to classify a journal or publisher as “predatory”, and perhaps, bring additional clarity to this discussion. Better methods of journal evaluation can be obtained when the factors that polarize journal evaluations are identified. As one example, we need to move away from simply using whitelists and blacklists and educate individual researchers about how to evaluate journals. This paper serves as an educational tool by providing more clarity about the “gray” publishing zone, and argues that currently available qualitative and quantitative systems should be fused to deterministically appreciate the zonation of white, gray and black journals, so as to possibly reduce or eliminate the influence of cognitive or “perception” bias from the “predatory” publishing debate.
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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 | 0.026 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.061 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".