Introduction to Daswani Tailors and Background of Interviewee
Bibliographic record
Abstract
For over four decades Daswani Tailors has provided custom tailored clothing to their clients all over the United States, Canada, Europe, Australia, and Asia. Daswani Tailors is headquartered in Portland, Oregon with a workshop in Kowloon, Hong Kong and travelling associates located throughout the United States. The project sponsor is Ken Daswani, the owner and master tailor of Daswani Tailors. From Mr. Daswani we learned about the history of the business, changes in the industry, and growth challenges. We interviewed him to get insight into the processes of this business and how he and his employees use their data. Daswani Tailors is like many small and medium-sized businesses – it is successful but faces challenges from technology and a rapidly changing industry. Mr. Daswani said “More and more people today would rather order from an application or have a monthly fashion package arrive at their door rather than the personal service we offer.” This resonated with our group because it seems that Daswani Tailors could benefit from analytical techniques which would give insight into their business operations, let them get more useful information out the data they have, and improve their bottom line.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".