A psychological perspective towards understanding the objective and subjective gray zones in predatory publishing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.074 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.084 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it