Editorial for special issue on education and humour: Education and humour as tools for social awareness and critical consciousness in contemporary classrooms
Bibliographic record
Abstract
It is not new to consider the instructive power of humour. Both Plato and Aristotle, through their superiority theories, saw the benefit of wit as a social corrective, although they remained suspicious of the uneducated laughter of the masses (Plato in Morreall 1987; Aristotle in Morreall 1987). This approach has informed traditions of satire and resistance humour in a myriad of contexts. Stott summarises the raison d'être of satire through its aim “to denounce folly and vice and urge ethical and political reform through the subjection of ideas to humorous analysis” (Stott 2005: 109). The political potential of humour is easily recognised as a rhetorical and communicative device, yet it seems odd that little stock has been placed academically or culturally in the idea of humour as an educative tool in other social and cultural contexts and, more specifically, in the classroom.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.019 | 0.018 |
| Insufficient payload (model declined to judge) | 0.049 | 0.023 |
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".