Using linguistic analysis to go below the surface in trademark disputes
Bibliographic record
Abstract
The notions of same or different are ubiquitous in trademark disputes. At issue is the likelihood of confusion between marks in the mind of the “average consumer.” The test for confusion rests on establishing their degree of resemblance in terms of “sound, appearance and ideas suggested.” Evidence adduced by forensic linguists typically centers on whether the marks contain the same word, share the same sounds, letters, and dictionary meaning, or share the same number of phones, phonemes, or syllables. But since the features appealed to are typically surface-level, and thus ostensibly also available to the layperson, the judge may decide that expert assistance is superfluous. I argue that reliance on such features to the exclusion of underlying linguistic structure may lead to misleading results. Drawing on various linguistic processes, I present several Canadian trademark cases in which I served as expert witness to demonstrate that different words (or collocations thereof) may in fact be instantiations of the same structure, while superficially like ones may be involved in entirely different constructions. The results of these analyses make a strong case for going beneath the surface in determining questions of same or different.
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 | 0.022 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.025 | 0.055 |
| Scholarly communication | 0.020 | 0.034 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".