Women and Media: A Study on the Marginalization of Female Discourse Power
Bibliographic record
Abstract
The mass media is the window for reflecting the mass ideology. The article makes a thorough analysis on the marginalization of female discourse power and studies the potential sexual discrimination of this phenomenon, regarding females as the subjects and the objects of the mass media, and then further traces its deep reason upon which many constructive suggestions and improved measures have been proposed. The article aims at awakening the masses to establish the correct sexual ideology in harmony and pursue the equality of discourse power between females and males. Key words: mass media; female discourse power; marginalization; sexual discrimination Resume: Les medias de masse est la fenetre pour refleter l'ideologie de la masse. Cet article fait une analyse approfondie sur la marginalisation du pouvoir du discours feminin et etudie la discrimination sexuelle inherente a ce phenomene tandis que les femmes sont les sujets et les objets des medias de masse. Il explore en outre la raison profonde de ce phenomene sur laquelle des suggestions constructives et des mesures ameliorees ont ete proposees. L'article vise a l'eveil des masses a fin d'etablir une ideologie sexuelle correcte et harmonieuse et de poursuivre l'egalite du pouvoir de discours entre les femmes et les hommes. Mots-Cles : medias de masse; pouvoir du discours feminin; marginalisation; discrimination sexuelle
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".