“Wild Beasts of the Philosophical Desert”: Religion, Science, and Spirituality in a Post-secular Age
Bibliographic record
Abstract
This article explores the popular genre of “spirituality and science” against the backdrop of the dominant religion-secular-science classifications. Science has come to exemplify secularity in the modern social imaginary, clearly differentiated from, and often in a conflictual relationship with, religion. The power and prestige of techno-science, tightly linked to the narrative of modern secular progress, helps to secure its claims to capturing what is real and true—over against religion that has increasingly lost traction on claims to the real. This article explores some representative figures in the genre of science and spirituality, considering the aims, dominant themes, and authorizing strategies of their writings. I argue that this largely grassroots movement challenges the dominant meanings and relations among religion, secular, and science in its quest to articulate an integrated vision. It turns to new scientific thinking, including quantum physics and biological sciences, and mystical experience to articulate an empirically grounded, and scientifically inflected and supported, spirituality. Through a series of cases, I show the variety within this popular genre that is challenging both traditional religion and modern secular science. Developing the optics, vocabularies, and sensibilities to make sense of these proliferating hybrid cultural formations remains a fundamental challenge.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.034 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| 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".