Shock charity campaigns: Building our understanding on their effectiveness: Comments on “Emotions and Prosocial Behaviors: A Study of the Effectiveness of Shocking Charity Campaigns” by Jeanne Albouy
Bibliographic record
Abstract
The recent article entitled “Emotions and Prosocial Behaviors: A Study of the Effectiveness of Shocking Charity Campaigns” authored by Jeanne Albouy published in Recherche et Applications en Marketing provides important evidence on the effectiveness of shocking advertisements in the context of charity appeals. Specifically, this research highlights how the negative emotions and empathy that are produced by shock campaigns lead to positive attitudes toward the ad, the cause, and the intentions to help the charity. This work is important because it helps to resolve a conflict in the literature – namely, divergent findings that have shown both positive and negative outcomes for ads in charity campaigns that are emotional and raw in nature. Furthermore, like any good research, this work also seeds new opportunities for future investigation into the role of shock advertising in the context of charity communication campaigns. This commentary will look to highlight where additional research opportunities exist. Specifically, the discussion will center on three directions of potential interest: (1) a broader investigation into the notion of shock advertising in the context of charity campaigns, (2) understanding potential differences in how an audience is exposed to shock advertising and the implications therein, and (3) contemplating other outcome measures that could provide meaningful insight into how shock advertising has an impact in the marketplace.
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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 | 0.019 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.017 | 0.026 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".