Technology, Trust, and Religion : Roles of Religions in Controversies on Ecology and the Modification of Life
Bibliographic record
Abstract
What does it mean to be human in a world of technology? What could be the role of religion in responding to the ecological crisis? Should we be concerned about the modification of food, and even of ourselves? Who do we trust to make decisions regarding our common future? What do we use our technology for? These are not questions for experts only. How can the wider public be involved? Do experts and the general public trust each other sufficiently? Or is the public ignorant, in the eyes of the scientists? And are too many engineers narrow minded, according to the general public? The contributors to this timely and necessary volume address expertise, trust and engagement, as we consider our technological condition , religious resources for the ecological crisis , biotechnology , and matters of trust between scientists and the general public. With contributions from leading scholars in the field, including James Miller from Queen's University, Canada and Tony Watling from the University College, London, this book will captivate a range of readers interested in the spirtitual dimension of of our culture and society
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".