Politics of representation in the digital media environment: presentation of the female candidate between news coverage and the website in the 2007 Korean presidential primary
Notice bibliographique
Résumé
Abstract This study analyzes how a female candidate was presented in the news media and on her campaign website, in order to compare the politics of gender representation in news coverage and campaign communication. Content analysis of news coverage of a Korean female candidate and the candidate's website shows that the female candidate was differently portrayed in the two media in presentations of personal trait frames, the linkage between issues and personal traits, and other gender-related characteristics, although the quantity of issue frames did not differ significantly. The findings suggest that although the news coverage still tends to reinforce gender stereotypes regarding a female candidate, the candidate used or articulated gender identities in her campaign website to oppose framing stereotypes in the traditional news media. Keywords: the Internet and politicspolitics of representationpolitics on the Webarticulationcampaign and strategic communicationpolitical public relationsdigital media Acknowledgments The author would like to thank Kideuk Hyun, Soo Jung Moon, and Renita Coleman for their invaluable comments and Joon Yea Lee for help in collecting the data for this study. An earlier version of this manuscript was awarded a Top Student Paper at the 2008 International Communication Association, Montreal, Canada. Notes 1. Tentative applications of articulation theory to female politicians' gender stereotypes are possible. Pertinent elements or identities can include presidential electoral campaigns, the media, female candidates such as Geunhye Park in Korea, a variety of gender stereotypes and frames such as masculine-identified issues/traits and feminine-identified issues/traits, media framing, and campaign strategies. These elements and identities form temporary unities or 'articulations', such as the articulation between female candidates and masculine stereotypes within a particular conjuncture. For more methodological application of the articulation theory, see Sikka (Citation2006) and Slack (Citation1996). 2. All of the newspapers chosen for analyses are grouped together since this study aims to examine whether and how a female candidate is presented differently in the news media and on the candidate's website. However, acknowledging a possibility that each newspaper may portray the female candidate differently, the researcher compared issue frames, personality traits, and primary focus of news stories between the three newspapers. No significant differences were found between the newspapers. 3. Because KINDS did not have articles of Chosun Ilbo it was necessary to search Chosun Ilbo website archives. 4. Issue categories and personal trait categories, which are mentions of issues or personal traits attributed to the female candidate, were collapsed into 'male-identified' or 'female-identified' issues and images to test research questions. While the agreement on these categories is not perfect, there is no doubt that literature has generally agreed to distinguish 'male-identified' and 'female-identified' issues and personal traits—that is, 'male-identified' issues include those issues where men are considered more capable (e.g., the economy, foreign policy, and homeland security) while 'female-identified' issues include those issues where women are seen as more capable (e.g., education, environment, and social security/welfare); 'male-identified' traits include those traits that are associated with men (e.g., competitive, tough, and aggressive) while 'female-identified' traits are those traits that are associated with women (e.g., compassion, sensitivity, and integrity) (see e.g., Kahn, Citation1994a; Kahn & Goldenberg, Citation1991). 5. After reaching a satisfactory agreement between the coders, the researcher coded more stories than a trained coder. However, the high level of reliability and the relatively straightforward nature of the coding variables may ease concerns about the validity and objectivity of the data. This limitation, nevertheless, should be noted; therefore, future research should be done with more robust coding procedure in terms of, for example, the number of sample stories assigned to coders.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».