Bibliographic record
Abstract
This book is the third in a series of publications that present the latest advancements in research on mass customization and personalization. Starting with Tseng & Piller (2003) and continuing with Piller, Reichwald & Tseng (2006), we again could collect the thinking of some of the leading scholars and practitioners in the field. In comparison to the previous editions, this is the most comprehensive collection of writings on mass customization ever. This inspired our publisher to name it the "Handbook of Research in Mass Customization & Personalization". The contributions in this handbook were inspired by the 4th World Conference on Mass Customization and Personalization (MCPC 2007), a biannual academic event that gathers the international research and practice community interested in mass customization, held in October 2007 at the Massachusetts Institute of Technology (MIT), hosted by the MIT Smart Customization Group (Mitchell et al. 2007). The conference also included a business seminar held at HEC Business School in Montreal, Canada. The participant roster of the conference represented the interdisciplinary nature of customization and personalization
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.063 | 0.029 |
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".