Bibliographic record
Abstract
Conspiracy theories have mainly been of interest to social epistemologists in terms of whether one can be warranted in believing them. In this literature, believing a conspiracy theory is often understood to mean endorsing some conspiratorial explanation of events. I argue that, in some cases, conspiracy belief is better understood as (dis)trusting sources of claims. To demonstrate this, I show that disputes over conspiracy theories possess a distinctive tendency (but not a necessity) to generate deep disagreement arising specifically from divergent attributions of trust in others. I explain this tendency in terms of agents’ desires to maintain consistency among their beliefs and avoid inquiring into their attributions of trust. Individuals who believe conspiracy theories often do not remain committed to the same conspiratorial explanations but do nonetheless remain firmly committed to attitudes of trust and distrust. By arguing that conspiracy belief often manifests as (dis)trust, I challenge a common assumption in the philosophy of conspiracy theories that conspiracy belief, understood as taking conspiracy explanations to be true, can always serve as an appropriate basis for asking when believing conspiracy theories is warranted.
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 | 0.012 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".