Editors’ Introduction: Science, Belief and the Sociological Tradition
Bibliographic record
Abstract
Science, belief and sociology Summarizing the sociological study of science and religion a little over 10 years ago, John H. Evans and Michael S. Evans (2008: 88) wrote: Although we know of no study of the comparative coherence of sociological research areas, we suspect that the field of religion and science is one of the muddiest in all of sociology. The conceptual source of this muddiness lies in the long-running academic assumption that religion and science always conflict and that they conflict over competing truth claims about the world. It is therefore hard for sociologists to analyze the relationship dispassionately because sociology itself was born as a scientific alternative to religion. While, as we will see shortly, there have been notable developments since this was written, Evans and Evans's point still stands. Sociological exploration of questions to do with science and religion (or, as we prefer to phrase it, science and belief) is extremely limited and scattered across largely isolated sub-disciplines. Beyond the US (where most research has focused), it would be hard to even describe it as a ‘field of study’, so limited and disjointed has research been to date. This is surprising in many ways because science and religion has been an abiding subject of public debate for many decades in many parts of the world, and there are numerous ways in which it intersects with sociologists’ concerns. Religious voices play a prominent role in many conflicts over science, from stem cell research to, most famously, evolution. There has also been a trend towards religious groups – from evangelicals (Toumey, 1994) to Muslim revivalists (Unsworth, Chapter 12, this volume), to Hindu nationalists (Thomas, Chapter 6, this volume) – justifying their beliefs by making a claim on science (von Stuckrad, 2014). At the same time, science has been deeply entangled in debates about secularization; many non-religious people see science as central to their identity (Lee, Chapter 8, this volume), while humanist and secularist organizations have regularly characterized themselves as ‘fighting in the name of science’ (Kind, Chapter 9, this volume). Yet, this subject has been on the margins of sociology for many years. It is hard to understand why this is without, as Evans and Evans say, taking into account the influence of what historians refer to as the ‘conflict thesis’.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.046 | 0.025 |
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".