Like and dislike. Negativity bias in political TV series
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
Abstract In this article I analyse people's comments about what they like most and least about two of the most popular political TV series, to determine in which way the content of the series (positive or negative) influences their answers. Results prove the existence of a negative bias in the case of series' opposite content as there is a clear difference between people's answers. The negative information triggered more reactions, people remembered more scenes, more details, analyzed more profoundly the double meanings and metaphors. On the other hand, people exposed to the positive series gave more general answers, remembered less details about characters and events. Abstrait Dans cet article, j'analyse les commentaires des gens sur ce qu'ils aiment le plus et le moins dans deux series politiques televisees, afin de determiner dans quelle mesure le contenu de la serie (positif ou negatif) a une influence sur leur reponses. Les resultats demontrent l'existence d'un biais negatif dans le cas du contenu oppose de ces series, il y a une difference claire entre les reponses des gens. L'information negative a declenche plus des reactions, les gens se souvenait plus de scenes, plus de details, ils ont analyse plus en profondeur le double message et les metaphores. De l'autre cote, les gens qui ont vu la serie positive ont repondu de mesure plus generale, avec moins des details sur les personnages et evenements.
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 | 0.000 |
| 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 | 0.000 |
| 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