Bibliographic record
Abstract
Marc Moreau was born in 1948 in Lewiston, Maine. His family is French Canadian, and the part of town where he grew up was a pocket for French-speaking people. It was referred to as a "Petit-Canada." Lewiston's economy centered on textile mills. All of his grandparents and both of his parents worked in the mills during their lives. His mother later became a nurse during World War II, and his father served in the army during that time but later returned to the mills. Moreau has a younger sister and brother. He delivered French newspapers as a boy and attended a grade school run by Dominican nuns. He attended Brothers of the Sacred Heart High School. He became an avid trumpet player at a young age and received a full scholarship to study music at the University of Connecticut. However, he realized that his musical skills did not match those of the other students, so he began to look for a different interest. He found one in philosophy, and proceeded on to Temple's doctoral program for philosophy. While at Temple he fought the draft for Vietnam and met his first wife. They divorced after six years and he met and married his current wife, Juliet. Juliet has two children from her first marriage, but he considers her children's children his grandchildren. At the time of the interview, he was chair of the philosophy department at La Salle University.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 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; 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".