Man Proposes, God Disposes: Recollections of a French Pioneer
Bibliographic record
Abstract
In 1910, young Pierre Maturié bid farewell to his comfortable bourgeois existence in rural France and travelled to northern Alberta in search of independence, adventure, and newfound prosperity. Some sixty years later, he wrote of the four years he spent in Canada before he returned to France in 1914 to fight in the First World War. Like that of so many youthful pioneers, his story is one of adventure and hardship—perilous journeys, railroad construction in the Rockies, panning for gold in swift-flowing streams, transporting goods for the Hudson’s Bay Company along the Athabasca River. Blessed with the rare gift of a natural storyteller, Maturié conveys his abiding nostalgia for a country he loved deeply yet ultimately had to abandon. Maturié’s memoir, Man Proposes, God Disposes, appeared in France in 1972, to a warm reception. Now, in the deft and marvellously empathetic translation of Vivien Bosley, it is at long last available in English. As a portrait of pioneer life in northern Alberta, as a window onto the French experience in Canada, and, above all, as an irresistible story—it will continue to find a place in the hearts of readers for years to come.
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 | 0.017 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".