Conclusion: Future Directions in the Sociological Study of Science and Belief
Bibliographic record
Abstract
One thing that this volume has made clear is that religious and other forms of ultimate belief interact with science in myriad ways, many of which are hardly recognized amid a public debate fixated on the simplistic categories of ‘acceptance’ and ‘rejection’ or ‘conflict’ and ‘compatibility’. For different religious and non-religious communities of belief, ‘science’ can function as a cause to be fought for, a means of bolstering the appeal and authority of a revivalist movement, a means of making subtle cultural or class distinctions, or an imagined adversary to be challenged or competed with. Among this volume’s central aims has been to broaden the horizons of the study of science and belief, expanding upon the recent, innovative, US-based sociological research and moving the focus to states such as India, Pakistan, Turkey, Sweden, Canada and the UK. The further one moves afield, the more complex the subject becomes: it is clear that there is a whole world of difference between, say, the Midwestern US educational settings discussed by David Long in Chapter 11 and the elite contexts in Renny Thomas’s study of Indian scientists in Chapter 6. Recent US-focused research has stridently moved away from the conflict thesis in the last decade, undermining the idea that public conflicts between conservative Protestants and secularist or scientific organizations are driven by knowledge differences. However, focusing the empirical lens on a country such as India – where it is unremarkable or even expected for elite scientists to see their findings and justify their work in religious terms – reveals this thesis to be provincial as much as it is simplistic.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".