42. The psychoneuroimmunophysiological responses to incongruous actions or statements/prevarications made for the purpose of eliciting rhythmic, spasmodic expiratory reflexes
Bibliographic record
Abstract
Although humans know instinctually that humour has healing powers, an understanding of the precise effects of humour and laughter had been largely unknown until the twentieth Century, due to the lack of technology. Not all of the barriers to research have been removed – it is still not possible to know “how much” good humour a person has or is experiencing – but there have been significant discoveries that help to prove that while laughter may not be the best medicine, it certainly helps the medicine go down.
 The understanding of humour has come in four distinct areas, and in periods that reflect the available technologies. With the discovery of laughing diseases, interest in humour drove Harry Paskind in 1932 to create a new machine to study muscle tone during good humour. From the 1950s to 1970s, the neurology of laughter was researched, accompanying further research into the pathology of laughter after an epidemic of laughter in Uganda from 1962-64. After this came the study of the immunology and the discovery that laughter fights cancer, with the champion of laughter research, William Fry, dedicating a decade from 1969-79 to this work. Finally came research on the indisputable effect of laughter – its healthy effects on human psychology. Thanks, in part, to this research, and also supporting it, the past forty years have seen men like Norman Cousins and Hunter “Patch” Adams bring humour into mainstream healthcare. This research provides doctors an opportunity both now and in the future, as we learn even more about humour, to bring a softer face to medicine and truly give patients something to smile about.
 Cousins N. Anatomy of an Illness. New York: WW Norton & Company, Inc., 1979.
 Paskind H. Effect of Laughter on Muscle Tone. Archives of Neurology and Psychiatry 1932; 28-3:623-628.
 Robinson VM. Humor and the Health Professions. New York: McGraw-Hill, 1991.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".