The Strategy Tool: The Trademark Map of Best 100 Brands in the World
Bibliographic record
Abstract
In this research, five steps are brought up to build up the trademark map, including (1) deciding sample range of trademarks, (2) analyzing the first-time information, (3) analyzing the second-time information, (4) building up the trademark map, and (5) analyzing the trademark map. This standard procedure can help enterprises create their trademark maps efficiently. A multi-dimensional scale is used for analyzing and building up the trademark map of the most famous one hundred brands, and 86 consumers are requested to proceed with the experiment of brand identification. The results are shown as follows. (1) To display the distribution of trademark samples clearly by building a visualized map, the level of trademark similarity between samples can be understood. (2) Enterprises can the apply trademark map for judging the identification and feasibility of their trademarks so that they are capable of avoiding tort and creating their own and only brand image.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 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".