Does accepting the theory of evolution mean there is no meaning of life?
Bibliographic record
Abstract
What is the meaning of life? Believe it or not, after more than ten years on the air, we at Why? Radio have never asked this question. But to make it more complicated, we want to know not just what it is, but how we can discover it in the age of evolution. If science gives us answers instead of religion, where do we look for meaning? Can Darwin provide us with what the holy scriptures have not? On this episode we will ask these very questions, while exploring the limits of science and going head to head with the most ineffable aspects of the human experience. Michael Ruse is a philosopher and historian of science. He taught at the University of Guelph in Ontario Canada from 1965 to 2000. Since then. Ruse has served as Lucyle T. Werkmeister Professor of Philosophy at Florida State University. He has written or edited almost fifty books including, The Darwinian Revolution: Science Red in Tooth and Claw, Can a Darwinian Be a Christian?: The Relationship between Science and Religion, and Darwin and Design: Does Evolution Have a Purpose? His most recent book, the topic of today’s conversation, is A Meaning for Life.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.064 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".