Bibliographic record
Abstract
Fortune Favours a Bieler is the colourful story of Philippe Bieler’s life and his long journey through the eventful twentieth century and beyond. It begins with his escape from war-torn Europe in 1941. Hand in hand with a number of prominent trailblazers, he went on to carve out a career in industry, banking, farming, and even politics. The tale transitions from aluminum in Canada to cranberries in Quebec and vineyards in France. Frequent failures are compensated by good cheer and some impressive successes. Bieler is a descendant of Swiss woodsmen and the son of a senior civil servant at the League of Nations. Born in 1933, he belongs to the silent generation, the cohort following the greatest generation and preceding the baby boomers, known for their thrift, respectfulness, loyalty, and determination. His outspoken mother and well-connected father raised him to be bold enough to grasp the fate he desired, a challenge he took up with vigour. He studied engineering at McGill University in Montreal and returned to his native Switzerland to pursue an MBA. He served as CEO at a number of industrial corporations, but he preferred his many ventures as an entrepreneur – and now, in his latest act, as an author, writing from his sheep farm in Wales. Fortune Favours a Bieler looks back on a century of abundant luck and opportunity for those who would seize it, through the life of one of its fortunate and passionate leading lights.
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 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.179 | 0.045 |
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".