Cultiver la différence
Bibliographic record
Abstract
What goes into making a life successful and what does success mean? If you think about a life as a chemical equation, then the elements are obvious: family, work, purpose. The key is discovering how to get the balance just right. In Cultiver la différence, Montreal entrepreneur and philanthropist Morris Goodman shares his personal and professional prescription for success and enduring happiness. Born in 1931 in Montreal to Ukrainian immigrants during the worst days of the Great Depression, Goodman recounts the events, strategies, and lucky breaks that led to a thriving company and a life of philanthropic accomplishments. From his first job as a pharmacy delivery boy to his graduation from the University of Montreal's Faculty of Pharmacy - when he had already started his own pharmaceutical company - through the crucial moments that created an international business, Goodman depicts stirring accounts of Montreal's Jewish community and the development of the global pharmaceutical industry. Along the way, he presents vivid, generous portraits of colleagues and business collaborators. Cultiver la différence is a powerful rags-to-riches story but it is also much more - it is a heartfelt, candid, and inspiring exploration of what makes our lives rich, what we value, and why.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".