Bibliographic record
Abstract
Protection of Industrial Models and Designs The Legal Regime in Argentina P. Kaufer-Barbe. Industrial Design Law in Australia S. Gilchrist, D. Ziegler. Industrial Design Protection in Brazil C. C. Schulz, A. Carminatti. Industrial Design Protection in Canada B. W. Gray, et al. Protection of Industrial Designs in China S. Feng. European Community Design Law R. Durie. Protection of Designs and Models in France A. Neri. Protection of Industrial Designs: An Overview of German Law K.-J. Michaeli. Protection of Design in India S. D. Ahuja. The Protection of Industrial Design Under Italian Law L. Pavanello. Japanese Design Law: A Summary for Practitioners of the Protection System Under the Design Law in Japan K. Asamura. Protection of Designs in Mexico J. Delgado. The Protection of Industrial Designs in the Netherlands R. Hermans. Protection of Industrial Design in the Nordic Countries S. Bigaard, K. E. Madsen. Industrial Design Law in Singapore G. Mirandah. Report on Industrial Designs in Spanish Law G. Ulloa. Legal Protection of Industrial Designs in Taiwan in a Nutshell P. I.C. Yun, F.F.J. Liu. The Law of Industrial Designs in the United Kingdom J. Linneker. Protecting Proprietary Product Designs in the USA D. W. Brinkman. Industrial Design Law in Venezuela L F. Elias.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".