The potential liability of the internet search engines deriving from trademark infringements
Bibliographic record
Abstract
The thesis deals with the potential liability of the Internet search engines deriving from trademark infringements. Only the particular topic of trademark infringements is covered, all other trademark related issues and their application to Internet search engines being excluded. Potential liability of the Internet search engines is studied in relation with metatag abuses, when involving search engines, and keyword advertising practices employed by search engines. All problematic of the thesis is assessed through a theoretical framework which looks to establish a liberal law and technology approach to Internet legal issues, as developed by the Canadian scholar Arhur J. Cockfield. Lawrence Lessig's ideas will play also an important role in understanding the implications of the thesis' topic. The thesis concludes that in the absence of an application of a law and technology approach courts run the risks of considerable misunderstandings and overstretching traditional legal values and interests.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".