Language, Labels and Lived Identity in Debates about Science, Religion and Belief
Bibliographic record
Abstract
In public discussion and polling on the subject of science and belief people’s views are often subsumed under identity labels such as ‘Creationism’, ‘Darwinism’, ‘New Atheism’, ‘Intelligent Design’ and ‘Theistic Evolutionism’. Often, these labels are held to accurately represent people’s views both by public figures and by social scientific researchers. In this chapter, Kaden, Jones and Catto make the case for a reassessment of the role of labels and the knowledge connected to them in popular and social scientific treatment of the relationship between science and belief. They argue that there are considerable problems in identifying people’s views using the majority of commonly used analytic labels. Drawing on 123 semi-structured interviews with scientists and members of the public in the UK and Canada from a range of religious and non-religious positions, the authors then show that such categories of belief are creatively interpreted. The authors highlight the limited salience of popular concepts in science and religion debates, showing that such terms are frequently unfamiliar to British and Canadian publics. Based on their analysis, they argue that naive application of labels contributes to misperceptions and prejudices, especially relating to religious people’s beliefs about human origins. Finally, they conclude that to limit such misperceptions attention needs to be paid by scholars to whether, how and why individuals relate their fundamental beliefs to aspects of 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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.090 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".