Bibliographic record
Abstract
Davies (emer., Univ. of Reading, UK) is perhaps the most eminent of the sadly limited number of sociologists who have considered humor worthy of serious study. Here he focuses on cataloging and analyzing various types of jokes, ranging from 'stupid and canny' through blond and lawyer to Jewish and Soviet-era jokes. The sheer coverage of this work is most impressive; after a close reading, it becomes clear that Davies's various interpretations deserve serious consideration. For example, he maintains that people who generally regard themselves at the center of things tell 'stupid' jokes about those they consider to be at the geographical and linguistic margins-hence 'Irish' jokes in the UK or 'Newfie' jokes in Canada. Other jokes are told about 'static' groups (aristocrats, peasants, dumb blonds) in a rapidly changing world. Davies makes two contestable claims. He warns correctly against absolutist interpretations of jokes: context is everything. More arguably, he claims that jokes have no social consequences. Narrowly, this might be so, but one could argue that his disregard of 'ideological framing,' whereby soft joke 'othering' facilitates harder versions, is unjustified. Throughout, Davies maintains a lively, provocative style-refreshing in a genre that is all too commonly soulless. Summing Up: Highly recommended. General readers and all levels of sociology scholarship. -Choice
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.025 |
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".