Consolation, Solace, and Leadership
Bibliographic record
Abstract
Human life is fleeting. We lose loved ones, our youth, and, well, everything else. What most people need more of is consolation: solace in the face of loss. On this episode, we explore the intellectual history of consolation, looking at how philosophers, artists, and even some politicians address the need for private and public comfort. From Cicero, to Abraham Lincoln, to Camus, we ask how the idea has evolved over time to be culture specific and idiosyncratic. Michael Ignatieff is a trained historian, a professor, author, broadcaster, and the former leader of the Canadian Liberal Party. He has written fiction, history, philosophy, and public commentary, and currently teachers at Central European University in Vienna, Austria where he served as Rector. He is the author, most recently of On Consolation: Finding Solace in Dark Times. During the conversation, Jack mentions one of his favorite books, When Bad Things Happen to Good People by Harold Kushner. You can find it here.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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; both teacher heads agree on what is shown here.
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".