Teaching What Society Needs: “Hacking” an Introductory Marketing Course With Sustainability and Macromarketing
Bibliographic record
Abstract
Marketing classes are often focused on the micro level, failing to account for wider societal issues. In this article, we argue for the inclusion of a wider macro-sustainability focus, one that “hacks” marketing education. With that objective in mind, we developed and delivered an introductory marketing course that integrated both the micro and the macro, thus infusing the course with macro-sustainability. This was done through an “expanded voice” perspective that included alternate complementary micro and macro class sessions while using a traditional managerial marketing textbook supplemented by macro-sustainability materials. We also integrated a controversies approach to support discussion and learning. We taught this course to 150 undergraduate students and conducted both quantitative and qualitative assessments of the course, including comparing results with an “unhacked” marketing course. Findings indicated increased awareness of macro-sustainability topics and movement on appreciation of sustainability and the role marketing can have in achieving this awareness. Finally, we offer a model of how marketing classes at all levels can be “hacked” with a macro-sustainability approach.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".