Winning the Battle but Losing the War: Ironic Effects of Training Consumers to Detect Deceptive Advertising Tactics
Bibliographic record
Abstract
Abstract Misleading information pervades marketing communications, and is a long-standing issue in business ethics. Regulators place a heavy burden on consumers to detect misleading information, and a number of studies have shown training can improve their ability to do so. However, the possible side effects have largely gone unexamined. We provide evidence for one such side-effect, whereby training consumers to detect a specific tactic (illegitimate endorsers), leaves them more vulnerable to a second tactic included in the same ad (a restrictive qualifying footnote), relative to untrained controls. We update standard notions of persuasion knowledge using a goal systems approach that allows for multiple vigilance goals to explain such side-effects in terms ofgoal shielding, which is a generally adaptive process by which activation and/or fulfillment of a low-level goal inhibits alternative detection goals. Furthermore, the same goal systems logic is used to develop a more general form of training that activates a higher-level goal (general skepticism). This more general training improved detection of a broader set of tactics without the negative goal shielding side effect.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".