Bibliographic record
Abstract
Alvin Segal is probably best known for his wonderful philanthropic activities.He is also much respected for his successful efforts to lobby governments and other public bodies in the interests and defence of the garment-manufacturing industry in Canada.Somewhat less known, perhaps diminished by the passage of time, are his early successes in revolutionizing the technology of tailored men's garment construction and production.Once upon a time, in the days before Alvin, the construction and production of tailored garments were driven by fashion designers and tailors, whose artistic inclinations added a certain mystique to the enterprise and thereby created a shield against the critical interventions of industrial engineers and production managers.There was much hand work, not enough efficiency, and high costs.Along came young Alvin, perhaps still wet behind the ears, untutored in the mysteries of tailoring and very much the iconoclast.With a clear and questioning mind, he saw better ways and had the tenacity to overcome the entrenched culture.The end result was that he created an engineered tailored garment of equal quality but at a much lower cost.Herein lay Alvin's true genius and the foundation of his amazing success in becoming the largest clothing manufacturer in North America.Hillel Becker, former president of Rubenstein Brothers (equipment suppliers to the garment industry)
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.463 | 0.354 |
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".