“A matter of bones and feathers, and wishful thinking”: Science and the Marginalization of Religion
Bibliographic record
Abstract
Abstract: When the American novelist Marilynne Robinson delivered the Terry Lectures in 2010, she described how religion is caricatured by some eminent scholars in the alleged conflict between science and religion: religion was just a matter of bones and feathers and wishful thinking. The widespread and popular view of science is that it renders traditions and practices such as religion obsolete. In this article, I explore the discourse used by science popularizers in their polemic against religion in their attempt to foreclose any alternative narrative to the narrative about science, in which religion is not the only villain; aboriginals and women are also enlisted as unwitting and naive “primitives.” By drawing on conversations in aboriginal thought—who are “naively” concerned with bones and feathers—and on conversations in feminist philosophy—who are all too familiar with being dismissed as engaged in wishful thinking—I consider their efforts to generate alternative narratives. My concern is how this discourse about science enables dehumanizing effects while precluding liberatory possibilities for human flourishing. It is here that I think philosophy of religion can make a significant contribution: to provide an alternative narrative to science's fiction about human beings as nothing more than “meaty machines” engaged in a competition to ensure the survival of their genes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.108 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| 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".