Bibliographic record
Abstract
Apparel Quality: A Guide to Evaluating Sewn Products, Second Edition is a user-friendly guide for evaluating apparel quality to ensure quality products that meet customer expectations. This book provides an overview of apparel production, emphasizing quality characteristics and cues, consumer influences, and motivations impacting purchasing decisions, and highlights the roles of product designers, manufacturers, merchandisers, testing laboratories, and retailers from product inception through the sale of goods. The text is highly illustrated to provide students with the tools needed to evaluate and critique quality elements in apparel and textile products skillfully. New to this Edition: - New fabric technology including e-textiles, sew bots, and automation - International common size equivalents to accompany U.S. size classifications by sex, height, and age - Sustainability considerations for raw materials, design development, and apparel production - Expanded international labeling and safety regulations and compliance for the United States, Canada, EU, and Japan Instructor Resources - The Instructor's Guide provides suggestions for planning the course and using the text in the classroom, including sample syllabi, in-class activities, lab activities, and projects. - The Test Bank includes sample test questions for each chapter - PowerPoint® presentations include images from the book and provide a framework for lecture and discussion Instructor's Resources may be accessed through Bloomsbury Fashion Central (www.BloomsburyFashionCentral.com). STUDIO Features: - Study smarter with self-quizzes featuring scored results and personalized study tips - Review concepts with flashcards of essential vocabulary and image identification - Watch videos that take you behind the scenes of factories and testing facilities, to see how concepts covered in the text are applied in the real world
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.296 | 0.143 |
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".