How grumpy cat helped students learn management concepts
Bibliographic record
Abstract
Purpose Management educators often seek out innovative ways to introduce theories and concepts in such a way that students are more engaged and connected with the course material. A meme is an image juxtaposed with short text that elicits emotional responses from its readers and is now a staple in social media. Examples include: grumpy cat, success kid and distracted boyfriend. The authors have successfully used memes both online and in-person as a teaching tool. This paper aims to describe how the authors have used memes and some of the best practices and lessons learned from this experience. Design/methodology/approach Students in a training and development undergraduate course and an organizational behavior MBA course were tasked with creating and presenting memes that reflected the subject matter in their respective courses. Findings Their fellow students were successful in identifying the course theory or concept when these student presenters presented their memes in class. This suggests that this type of activity is helpful for students to apply a key course concept or theory in a way that was fun and interactive. Follow-up feedback from the students indicated that they enjoyed this type of activity and felt that it aided in their retention of course material. Originality/value While memes are quite popular in social media, there is a paucity of academic articles on the application of memes for teaching management concepts. This article guides instructors on how the authors have used memes in the classroom and offers some suggestions for doing a debrief afterward.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".