Leading Globally: Giving Oneself for Things Far Greater Than Oneself
Bibliographic record
Abstract
When I was 11 years old, my Austrian mother explained to me that when she was my age she had wanted to have at least 6 children. Yet by the time she met my American father, just 8 years later, she no longer wanted any children. Losing most of her friends and family during World War II to Hitler's terror had convinced her that the world was not a fit place to raise children. Luckily, especially from my perspective, my father convinced my mother that within the family the two of them could create a bubble of love, and within that bubble their children could grow up in safety and happiness, protected from the inhumanity raging outside. Having grown up within the bubble of their love, and in sunny southern California rather than war tom Europe, I never doubted that our role on earth, as human beings and as leaders, was to expand the bubble to encompass the world: or as the rabbis would exhort us, to return to our original task of Tikun Olam, the restoration of the world. [...]
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.013 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.059 | 0.017 |
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".