Bibliographic record
Abstract
A second-order conspiracy (SOC) is a conspiracy that aims to create (and typically also disseminate) a conspiracy theory. Second-order conspiracy theories (SOCT) are theories that explain the occurrence of a given conspiracy theory by appeal to a conspiracy. In this paper I argue that SOC and SOCT are useful and coherent concepts, while also having numerous philosophically interesting upshots (in terms of epistemology, explanation, and prediction). Secondly, I appeal to the nature of two specific kinds of second-order conspiracies to make the case for what has been called ‘local generalism’ (Stamatiadis-Bréhier Citation2023a). Specifically, I focus on so-called ‘denial industries’ to argue that the structure of these second-order conspiracies allows us to infer non-accidental generalisations about the domain of conspiracy theories. Even though it is true that there is nothing epistemically problematic with the general class of conspiracy theories, there are specific subsets of conspiracy theories that warrant immediate strong suspicion (cf. Dentith Citation2022). By looking at the intricate mechanisms by which these denial industries operate, we can infer that the conspiracy theories that are produced by them are epistemically unwarranted. I conclude by making some exploratory remarks about what the metaphysics of second-order conspiracies would look like.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.041 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| 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".