Bibliographic record
Abstract
Modern trademark scholarship and jurisprudence view trademark law as an institution aimed at improving the amount and quality of information available in the marketplace by reducing search costs. By providing a concise and unequivocal identifier of the particular source of particular goods, trademarks facilitate the exchange between buyers and sellers, and provide producers with an incentive to maintain their goods and services at defined and persistent qualities.Working within this paradigm, this Article highlights that reducing search costs and providing incentives to maintain quality are related yet distinct functions and shows that recognizing their distinct nature enriches our understanding of trademark law. The Article first develops a distinction between two functions of trademarks: a linguistic and a trust functions. Then, the Article demonstrates how the distinction provides a matrix for evaluating the normative strength of various trademark rules and doctrines. Under this matrix, rules that promote both functions would be considered normatively strong; rules that promote neither function would be normatively weak; and rules that promote one function but not the other would be normatively ambiguous, their strength depending on the results of a closer cost-benefit analysis.
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 | 0.014 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.043 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".