Why Does Dilbert, the Far Side, and other Cartoons Convey Essential Truths about Human Factors and Ergonomics?
Bibliographic record
Abstract
The purpose of this panel session is to explore how cartoons have been used to teach and discuss essential truths about human factors and ergonomics. Naturally, we hope to have fun too. Human factors and ergonomics is often much too serious. Educators and non-educators alike should enjoy the material. Numerous philosophical and practical issues are likely to emerge as the session evolves. The audience, we hope, will be an active participant in the laughter and discussion. Each panelist has been invited to open their lecture files and share their favorite cartoons about a variety of topics such as office ergonomics (see, e.g., Dilbert), aviation displays and controls (see, e.g., the Far Side), industrial ergonomics, human-computer interaction, information design, human error, transportation human factors, medical systems, and so forth. Each panelist brings a unique research and experiential perspective to the panel. For example, a variety of nationalities including the U.K., Canada, Australia, and the U.S. are represented. In addition, panelists will be invited to discuss a number of deeper issues. Are cartoons a useful teaching tool? Can a quiet class be roused from their slumber by the use of visual humor? Is there a best way to introduce or use a cartoon in a lecture? What copyright issues surround the use of cartoons for educational use? Why do we laugh (or cry) at cartoons that succinctly capture poor design in human factors and ergonomics? Can the sting of a poor design evaluation be moderated by the use of humor?
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
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".