Bibliographic record
Abstract
Traditionally, when managers have been considering whether to file a lawsuit, their attorneys have advised them on factors such as the likely costs of a suit and the probability of obtaining damages. However, the authors note, companies today may want to consider an additional factor: the possible marketing consequences ? positive or negative ? of a given lawsuit. In particular, the authors discuss the marketing implications of lawsuits between competitors or potential competitors. They consider the marketing ramifications of a lawsuit between two rival pizza chains, Pizza Hut Inc. and Papa John?s International, over advertising claims made by Papa John?s ? and conclude that publicity surrounding an initial verdict in Pizza Hut?s favor (a verdict later overturned) generally conveyed Pizza Hut?s perspective to the public, presumably with more credibility than similar advertising would have. The authors also examine a trademark dispute between Starbucks Corp. and the owner of the Old Quarter Acoustic Cafe, a bar in Galveston, Texas, which markets ?Starbock? beer. In this case, they observe that Starbucks faced the marketing risk of appearing to be a bully. The authors also explore how large corporations in suits with smaller rivals may face a risk of negative publicity, while small companies may face financial risks.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".