Bibliographic record
Abstract
In 1951, Alvin Cramer Segal, at the age of eighteen and without a formal education, started working in the factory of his stepfather’s company in Montreal. Today he is the chairman and chief executive officer of the largest supplier of men’s fine-tailored clothing in North America, and is considered an outstanding business and community leader, at the forefront of policy-making in Canada’s apparel industry, with commitments to philanthropic efforts that echo his business accomplishments. In My Peerless Story, Segal recounts how he learned business from the collar down and from the ground up, transforming a family-owned business into one that would eventually come to licence labels such as Ralph Lauren, Calvin Klein, and Michael Kors. Sharing anecdotes and personal experiences, Segal describes the history of garment manufacturing in Montreal and his intuitive strategies to leverage growth by improving fabrics, and adapting to innovative changes in the industry, eventually becoming the main inventory source of designer label suits to major department stores. Written from the heart, not as a handbook but rather as the story of a well-suited business career, My Peerless Story nonetheless includes relevant business lessons for the aspiring and inspired.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.053 | 0.024 |
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".