Using Cartoons to Teach Corporate Social Responsibility
Bibliographic record
Abstract
Changing curriculum content requirements, based on shifting global perspectives on corporate behavior and capitalism as well as business school accreditation requirements, mean that many marketing instructors have attempted to introduce discussions of organizational ethics, corporate social responsibility, and corporate governance into their classes. How these issues are addressed will, of course, depend on the instructor, the course, the level of the students, and the time available during the course to discuss the issues. Whether ethical issues in marketing are introduced as part of an existing class discussion, as a separate weekly subject topic, or as an entirely dedicated course, we recognize that it can be difficult to get students actively engaged and involved. In this paper, we present an alternative and interactive in-class exercise using group analysis and discussion of imagery and symbolism—understood as a reflection of public sentiment—in political cartoons. We introduce theories of cartoon analysis as social commentary, describe the exercise and methods, and then illustrate an example of the exercise as conducted with our own students. We conclude by noting the method’s limitations and considering alternative pedagogical applications of the analytical framework.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.054 | 0.006 |
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".