Who is Not Afraid of Richard Dawkins? Using Google Trends to Assess the Reach of Influential Atheists across Canadian Secular Groups
Bibliographic record
Abstract
Google Trends data on search volumes of high profile atheist public figures are used to assess their relative reach among different types of seculars in Canada. The user query data mined from Google Trends are complemented with an extensive set of information extracted from the Canadian General Social Surveys of 2005 to 2016. The analysis shows that the reach of high profile atheists is positively correlated with the presence of strictly-seculars, a proxy for the atheists. The investigation produces no statistically significant correlation between the presence of other types of seculars, namely the unchurched believers and the nominal affiliates, and the search volumes for high profile atheists. This exploratory finding suggests that the influential atheists likely “preach to the choir,” catering to like-minded individuals, at the exclusion of those with relatively close but differing views on religion and spirituality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".