Transforming Lives at Sheridan : a Tribute to Dr. Mozammel Khan
Bibliographic record
Abstract
The original 2017 edition of this title was published in recognition of Dr. Mozammel Khan’s career in quality assurance instruction and positive impact on graduates of Sheridan College’s Quality Assurance Manfacturing Management program (PQUAS), with proceeds from the book supporting the Mozammel Khan scholarship Foundation. Khan, originally from Bangladesh, taught in Singapore before immigrating to Canada in the 1990s, where he founded the first post-secondary QA program of its kind in Ontario at Sheridan College.\nEditor Lorraine Fraser collects heartfelt letters of appreciation to Khan from alumni of the PQUAS program, who share memories, successes and challenges from their time as students at Sheridan, as well as describing his impact on their careers in the field. In an autobiographical chapter, Khan shares his background in engineering and quality assurance, his journey as an immigrant, and his involvement in developing Sheridan’s PQUAS program. Dr. Iain McNab (Dean of Faculty of Applied Science and Technology) further discusses Khan’s personal history and career at Sheridan in an interview, while his political life and impact for other new immigrants are explored by Professor Jack Urowitz (Faculty of Animation, Art and Design) and Dr. Soumitra Nandi (Faculty of Applied Science and Technology).
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.022 |
| Insufficient payload (model declined to judge) | 0.019 | 0.011 |
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".