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.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".