Particular popular science: British scientists writing, speaking and broadcasting on science and religion from the 1980 <scp>s</scp>
Bibliographic record
Abstract
Abstract This paper draws on extended life story oral history interviews with scientists who, beginning in the 1980s, turned to writing popular books, making radio and television programmes and taking to the stage for public lectures and debates, with relations between science and religion often a key topic: Peter Atkins, Nicholas Humphrey, Steve Jones, John Polkinghorne, Russell Stannard and Lewis Wolpert. I show that these interviews capture aspects of motivation and experience missed in much existing work on popular science. Stressing historical and individual particularity, I argue that what these scientists say about their decisions, aims and rewards should make us question a strong tendency in recent scholarship both to regard popular science as part of scientific work in general, and also to read the outcomes of popular science – such as advocacy for science or the promotion of certain theories – as the motivations for its production.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".