Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>The 26-year-old Tunisian street vendor, Mohammad Bouazizi set himself ablaze, sparking demonstrations and revolutions in more than one country in the Arab world. Protestors utilized all possible forms of expression to give an outlet for the long suppressed feelings, attitudes and thoughts. A new social discourse emerged where freedom of expression was imposed. Tunisia led the move and was followed by Egypt, Libya, Yemen and Syria. Though these revolting countries have many things in common, each has its own touch to the mosaic portrait of the new social discourse. Political satire has its place in this discourse where political humor has witnessed a tremendous boom. The present study was based on data obtained from cartoons, slogans, digital discourse and jokes. It investigated the use of humor by protesters in the Arab Spring countries, the linguistic devices employed and the role of intertextuality. The study revealed that humor was used in the Arab Spring countries to denigrate the presidents. Sources for intertextuality included proverbs, songs, poetry and commercials. Humor made use of some linguistic devices such as puns, synonyms, antonyms, lexical ambiguity and rhyme.</p>
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.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it