Bibliographic record
Abstract
Gender differences permeate every aspect of human personality and appearance, and dictate how men and women should act, think and behave. Gender embodies a pattern of relations that evolves over time to define male and female, masculinity and femininity, concurrently structuring and regulating people’s relation to society. Gender decides what is expected, permitted and valued in a woman or a man in a given context. This paper discusses male and female aggression in political debates with a special focus on the recent debate held on 9th September 2021 in Canada. The analyses carried out through the methods of content, discourse and pragmalinguistic analyses, show that aggression is frequently categorized as a social behavior, and thus falls within a set of criteria depending on the roles that people occupy. In the world today, there is an increase in the use of communicative aggression, both verbal and non-verbal in the political arena. Aggression is widely applied in political communication where the main purpose is to fight the opponent and get the attention of the audience and voters. Male politicians are expected to be verbally and non-verbally more aggressive than women while female politicians perceived as less aggressive and considered to be better performers. However, this assumption remains an area of contention.
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.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".