Pseudo or perish: problematizing the ‘predatory’ in global health publishing
Bibliographic record
Abstract
In this paper, case story methodology is used to construct the narrative of a publisher of scholarly journals. Real-world examples are compiled within a single fictionalized narrative to enable identification of salient contextual features to help identify boundaries and points of difference between forms of pseudo and legitimate or credible scholarly publications. Moving beyond a distributional lens, Eric Hobsbawm’s theory of social banditry is contrasted with neoliberalism and applied to problematize the demonization of an array of publishing practices labeled as predatory. How some vehicles of open access publication come to be understood as exploitative within academe’s hierarchies of prestige can reflect forms of stigma and discrimination not wholly evident in status quo discourse regarding publication in scholarly journals. In the absence of ethnographic evidence, the case story methodology—itself a manifestation of pseudoscience—is found to be an adept method with which to consider the global health problem of predatory publishing.
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 | 0.034 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.021 | 0.103 |
| Scholarly communication | 0.024 | 0.031 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.007 | 0.008 |
| 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".