Can Novel Findings from Emerging Neuroscientific Technologies be Incorporated into Trademark Law in Canada?
Bibliographic record
Abstract
American scholar, Mark Bartholomew, predicted in 2018 that a new kind of neuroscientific evidence would help businesses involved in lawsuits connect their trademarks with the public’s perception of their trademarks. Bartholomew coined the term "neuromarks’ for this evidence. Bartholomew focused on U.S. trademark law. This research demonstrates, looking at both Canada’s domestic law and Canada’s relevant international treaties and trade agreements, that such evidence has not yet been used (in 2022) in trademark litigation in Canadian courts or tribunals but that there appears to be no legal barrier to its use in future in Canada. This research notes that neuroscience literature indicates that, while Bartholomew discussed “neuromarks” as a future concept, from the neuroscientific perspective, it is already scientifically possible to obtain evidence of individuals’ connections between marks and specific goods and services: it only awaits litigators in Canadian cases introducing such evidence and Bartholomew’s “neuromarks” can become a reality.
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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 | 0.019 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.020 | 0.024 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 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".