Bibliographic record
Abstract
This study attempts to create a clear definition of the term "conspiracy theory" using a survey method. The term sees currency in media and social settings, especially with the proliferation of the internet and events such as 9/11, but there is not an adequate understanding of how the term is used. Queen's students were surveyed to determine conditioning factors in their usage of the term. The students were presented with a set of fifty hypothetical claims that a certain event had occurred or is occurring, and then were asked whether the claim is a "conspiracy theory" to them or not. Fifty‐nine students were surveyed. The analysis of the resulting data reveals that collectivity is not a conditioning factor for usage; the actions of groups and individuals were treated almost identically, with both consistently deemed "conspiracy theories" in the data. Specific factors, such as the presence of an assassination or aliens, were identified. Hypothetical claims designed to be "strange" also scored consistently high. Gender was ruled out as a factor, as was the nation implicated in the claim, either Canada or the United States. The results show that the term has specific factors which condition usage. Also, the unimportance of collectivity as a factor contradicts most academic definitions of the term. What this study might have revealed is that there is a vernacular, popular usage that differs from the academic usage. 27
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".