72 Wisdoms: A practical guide to make life more meaningfu
Bibliographic record
Abstract
How do you create a good life, a life true to your values, a beautiful and meaningful life? This is a guidebook to help you do that. Wisdom is offered to enhance your spiritual, psychological, and philosophical health. The 72 Wisdoms cover 72 concerns all thinking people care about. The wisdoms are quotes which introduce particular topics of concern. They come from sources as diverse as Old Testament proverbs, ancient Greek and Roman aphorisms, Quaker values, a Beatles song, neuroscience, astrophysics, and memorable lines from classic flicks and TV shows. The people quoted are also a diverse lot, including, for example, Jesus, Muhammad Ali, the Dalai Lama, Betty White, and Stephen Hawking.The topics covered range from ordinary, daily concerns to the deepest philosophical questions. How should you deal with boredom? What is the “golden mean” of balancing living for today, planning for the future, and learning from the past? Does God exist? What is the evidence of an afterlife? How do you become your highest and best self? Which has more value, winning or doing your best? What constitutes an existentially meaningful life? Why do people vote for tyrants like Putin and what should be done about the Russian invasion of Ukraine? What is the greatest danger to democracy, and what is the greatest danger to the economy? Topics relevant to each stage of life are covered: giving birth to a baby, falling in love, and parenting, as well as grieving the loss of loved ones, and preparing for your own death.Jeff Rasley tells the story behind each quote and then teases out more profound meanings than the conventional ones. He enlivens the discussion by drawing on his own personal experiences. The book offers inspiration, advice, includes humorous observations, and asks probing questions. It will deepen your understanding of how to live a meaningful 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.108 | 0.100 |
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".