Bibliographic record
Abstract
This book grows out of thirty years of teaching in the Department and Centre for the Study of Religion at the University of Toronto.My academic career provided me with the opportunity to exchange ideas with many fine students whom I attempted to instruct in religious ethics.I am grateful to them all for helping me clarify my thoughts on ethics and technology using an analysis inspired by the writings of George Grant.Perhaps the most remarkable student who enrolled in my courses was Scott Marratto.I learned so much from him that, when my energy and enthusiasm were flagging, I asked him to act as my research assistant and help me complete a manuscript that had been too long in coming to birth.In the process, Scott became the coauthor of this book.He was almost wholly responsible for the just war analysis of modern weapons in the latter part of chapter 5.He contributed chapter 7, which provides essential insights into the new eugenics movement and its implications for the disabled.He made substantial contributions to, and rewrote sections of, chapters 3, 8, and 9.He did much to improve the coherence, argumentation, and style of the entire manuscript.Without Scott Marratto's collaboration, The End of Ethics in a Technological Society would, I fear, have remained on my hard drive.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.296 | 0.230 |
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".