Bibliographic record
Abstract
Purpose The purpose of this paper is to show that a new size labelling system based on the data gathered by [TC]2in the Size USA, Let's Size up Americasurvey would better serve the female population than the system currently in use. Design/methodology/approach Based on previous research conducted on [TC]2data and on pants measurements in the Canadian market, a new labelling system is proposed where size information is provided with three specific body measurements along with a female silhouette pictogram. Findings The paper demonstrates that a size label showing three pants measurements: pants waist, approximate hips, and inseam length, accompanied by a silhouette identifying where these measures were taken, is highly predictive of fit. Research limitations/implications The study was limited to lower body (pants) for female. Practical implications A change to such a size‐labelling system would allow the apparel industry to move towards mass customisation at minimal costs. It would be more effective for the apparel order givers and retailers, enabling them to target whichever market they wish yet convey the necessary fit information in a generally accepted format. This system would also be more efficient as it would reduce the consumer time spent in fit identification and merchandise returns, in the case of internet or catalogue sales. As a corollary, it would increase both consumer shopping experience satisfaction and industry profitability. Originality/value The study proposes a new labelling system.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".