Bibliographic record
Abstract
In this paper I develop a genealogical approach for investigating and evaluating conspiracy theories. I argue that conspiracy theories with an epistemically problematic genealogy are (in virtue of that fact) epistemically undermined. I propose that a plausible type of candidate for such conspiracy theories involves what I call ‘second-order conspiracies’ (i.e. conspiracies that aim to create conspiracy theories). Then, I identify two examples involving such conspiracies: the antivaccination industry and the industry behind climate change denialism. After fleshing out the mechanisms by which these industries systematically create and disseminate specific types of conspiracy theories, I examine the implications of my proposal concerning the particularism/generalism debate and I consider the possibility of what I call local generalism. Finally, I tackle three objections. It could be objected that a problematic genealogy for T merely creates what Dentith (Citation2022) calls ‘type-1’ (or ‘weak’) suspicion for T. I also consider a challenge according to which the genealogical method is meta-undermined, as well as an objection from epistemic laundering.
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 | 0.021 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.039 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".