ON THE PREVALENCE AND IMPACT OF VAGUE QUANTIFIERS IN THE ADVERTISING OF CAUSE-RELATED MARKETING (CRM)
Bibliographic record
Abstract
A series of three studies examines potential consumer confusion associated with the advertising copy used to describe cause-related marketing (CRM) campaigns, where money is donated to a charity each time a consumer makes a purchase. The first study assesses the relative frequency of various copy formats in CRM on the Internet. The authors find that the majority of the copy formats (69.9%) are abstract (e.g., a portion of the proceeds will be donated), 25.6% are estimable (e.g., X% of the profits will be donated), and 4.5% are calculable (e.g., X% of the price will be donated). Subsequent studies find that (1) slight variations in abstract wording in advertising copy leads to considerable differences in consumers' estimates of the amount being donated, (2) the amount of the donation estimate for each abstract copy format varies considerably across individuals, and (3) the donation amount can impact choice. Taken together, the three studies demonstrate that the vast majority of advertising copy used to describe CRM donations is abstract, that different but legally equivalent abstract copy formats result in large differences in mean perceived donation level, and that these donation levels can impact consumer choice. Implications for advertising strategy and public policy are discussed.
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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 | 0.052 | 0.235 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".