Bibliographic record
Abstract
Contemporary culture has seen an increase in the influence of fringe beliefs, chief among them pseudosciences: doctrines that masquerade as sciences. In light of the myriad ways in which the work of the public sector is intertwined with and depends upon that of scientists, it is essential that policymakers be able to recognize these pretender sciences. However, the academic literature has yet to yield a widely accepted and easily applicable definition of “pseudoscience”. This paper proposes that pseudosciences are most adequately characterized by their origin in social contexts in which there is little open, critical discussion of ideas: hence, in contrast with genuine science, there can be no assumption by non-scientist observers that pseudosciences have withstood criticism prior to their promulgation as knowledge. The applicability of this proposal is demonstrated with a case study, where it is used to identify the pseudoscientific features of Andrew Wakefield’s “anti-vaccine” advocacy
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.050 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".