Resolving Ambiguity of Some Egyptian Political Jokes Contemporaneous with 2011 Revolution
Bibliographic record
Abstract
This paper focuses on resolving ambiguous jokes that were contemporaneous with 2011 revolution in Egypt. It observes that some jokes are easily understood by some and are unintelligible to others. The current study is a qualitative descriptive research that depends on collecting and analysing qualitative data. The data is classified into three groups each of which represents a group of ambiguous jokes corresponding to a degree on a proposed scale of specificity. This scale comprises linguistic and socio-pragmatic aspects that contribute to disambiguating such jokes and achieving the humorous effect. It finds that understanding a joke in general and an ambiguous one in particular depends not only on linguistic aspects. It also finds that the integration of the scale of specificity into the analysis proves crucial. The degrees on the scale help explain why a joke is understood by some and incomprehensible to others. Those degrees are relative in that what is deemed as a general degree may be regarded as a specific one to others.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".