Bibliographic record
Abstract
This article aims to answer two questions: should business competitors be allowed to use each other’s goodwill in this way and, if so, can trademark law police the program without stifling competition? Part I examines the technical aspects of the AdWords program. Part II explores the underlying rationales of trademark law to start developing a normative position. Part III reviews the American jurisprudence and commentary to hone that normative position and to identify a compatible legal framework. Part IV compares that framework against Canadian law.\nThis article endorses the work of Misha Gregory Macaw who, unlike some trademark expansionists, argues that keying is permissible provided that it does not confuse buyers as to source. The implications of his position are that trademark law should apply to all instances of keying to prevent abuse, but that intervention depends on the likelihood of confusion, not simply one seller profiting from another’s goodwill. A survey of Canadian commentary and jurisprudence suggests that Canadian trademark law is compatible with Macaw’s thesis: the tort of passing off appears well suited to disciplining trademark use on the Internet and, although some provisions of the Trademarks Act could be expanded to prohibit socially beneficial uses of competing marks, Canadian courts have applied them reservedly, especially compared to some of their American counterparts.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.021 | 0.014 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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".