What science means to me: Understanding personal identification with (evolutionary) science using the sociology of (non)religion
Bibliographic record
Abstract
Within science and technology studies, there is an established tradition of examining publics’ knowledge of, trust in, access to and engagement with science, but less attention has been paid to whether and why publics identify with science. While this is understandable given the field’s interest in bridging gaps between publics and producers of scientific knowledge, it leaves unanswered questions about how science forms part of people’s worldviews and fits into cultural politics and conflict. Based on 123 interviews and 16 focus groups with mixed religious and nonreligious publics and scientists in the United Kingdom and Canada, this article utilises approaches from the sociology of (non)religion to delineate varieties of science identification. It maps out ‘practical’, ‘norm-based’, ‘civilisational’ and ‘existential’ identifications and explores how these interrelate with people’s social characteristics. The article illustrates how science identification is typically dependent on a constellation of cultural/political influences rather than just emerging out of interest in science.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".