Convenient Pigeon Holes? The Classification of Trade Marks in Historical Perspective
Bibliographic record
Abstract
The Nice Agreement Concerning the International Classification of Goods and Services for the Purposes of the Registration of Marks was signed on 15 June 1957. It sets out the procedural requirements for the Nice Classification, a system limited in its substantive requirements towards harmonisation. This has directly resulted in differences in its scope and meaning, within the context of national registrations, between States party to, or making use of, the Agreement. The history of classification of trade marks places the origins and gradual conceptualisation of classification alongside the development of substantive trade mark law. The legal analysis on the Nice Agreement, together with the case studies of Mexico, Turkey, Japan, Canada and the UK highlights the differences in its interpretation by economically disparate countries. It is argued that the intended function of trade mark classification has become lost in the translation of the Nice Agreement into diverse legal systems. “But when all has been said, it is not easy in any human activity to lay down a rule so well grounded on reasoned argument that Fortune fails to maintain her rights over it.” 1 Michel De Montaigne (1533-1592)
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".