The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Gender Differences in Negative Campaigning: The Impact of Party Environments
Open accessAbstract
How does gender affect the attack strategies of political actors? Do men and women diverge in their propensity to go negative and in their choice of targets? Extant research has long sought to shed light on these questions (e.g., Brooks 2010; Kahn 1993; Krupnikov and Bauer 2014; Proctor, Schenck-Hamlin, and Haase 1994; Walter 2013). Among all the possible determinants of attack behavior in elections, candidate gender has been one of the most “heavily studied” (Grossmann 2012, 2). However, the relevant research focuses almost exclusively on the United States and therefore on a system with candidate-centered campaigns, weak party organizations, and winner-takes-all competitions. Notwithstanding the importance of the USA as a case and exporter of campaign techniques, such context is specific and likely to bias the results. The few pioneering studies that examine the role of gender in negative campaigning outside the U.S. (Carlson 2001, 2007; Walter 2013) have addressed this question mostly by transferring the analytical framework of U.S.-based research to other political systems. Consequently, they have barely begun to incorporate the distinctive features of multiparty systems and strong party organizations as determinants of gender differences in attack behavior. The present article provides a novel argument about the role of party environments as a crucial context factor in party-centered political systems. Specifically we argue that in party-centered campaigns the gender balance within parties influences differences in the attack behavior of male and female politicians.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Role congruity theory of prejudice toward female leaders.
Eagly, A. H., & Karau, S. J. (2002) · Psychological Review · doi:10.1037//0033-295x.109.3.573 · Cited by 7,300
Some Effects of Proportions on Group Life: Skewed Sex Ratios and Responses to Token Women
Kanter, R. M. (1977) · American Journal of Sociology · doi:10.1086/226425 · Cited by 3,154
Does sexual selection explain human sex differences in aggression?
Archer, J. (2009) · Behavioral and Brain Sciences · doi:10.1017/s0140525x09990951 · Cited by 607
A Simple Multivariate Test for Asymmetric Hypotheses
Clark, W. R., Gilligan, M. J., & Golder, M. (2006) · Political Analysis · doi:10.1093/pan/mpj018 · Cited by 120
Cited by
Messing, S., & Westwood, S. J. (2014) · Communication Research · doi:10.1177/0093650212466406 · Cited by 902
A stage to engage: Social media use and corporate reputation
Dijkmans, C., Kerkhof, P., & Beukeboom, C. J. (2015) · Tourism Management · doi:10.1016/j.tourman.2014.09.005 · Cited by 515
Data Capitalism: Redefining the Logics of Surveillance and Privacy
West, S. M. (2019) · Business & Society · doi:10.1177/0007650317718185 · Cited by 488
Ahn, J. (2011) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21540 · Cited by 356
Community‐Based Innovation Contests: Where Competition Meets Cooperation
Bullinger, A. C., Neyer, A., Rass, M., & Moeslein, K. M. (2010) · Creativity and Innovation Management · doi:10.1111/j.1467-8691.2010.00565.x · Cited by 336
Twitter reveals human mobility dynamics during the COVID-19 pandemic
Huang, X., Li, Z., Jiang, Y., Li, X., & Porter, D. (2020) · PLOS ONE · doi:10.1371/journal.pone.0241957 · Cited by 242
Evaluating the use and impact of Web 2.0 technologies in local government
Sivarajah, U., Irani, Z., & Weerakkody, V. (2015) · Government Information Quarterly · doi:10.1016/j.giq.2015.06.004 · Cited by 152
Kim, Y., & Glassman, M. (2013) · Computers in Human Behavior · doi:10.1016/j.chb.2013.01.018 · Cited by 134
Vandenbosch, L., & Eggermont, S. (2016) · Communication Research · doi:10.1177/0093650215600488 · Cited by 122
Limaj, E., Bernroider, E. W. N., & Choudrie, J. (2016) · Information & Management · doi:10.1016/j.im.2015.12.003 · Cited by 72
GALVEZ, C. (2018) · Transinformação · doi:10.1590/2318-08892018000300001 · Cited by 68
Deriving a typology of Web 2.0 learning technologies
Bower, M. (2016) · British Journal of Educational Technology · doi:10.1111/bjet.12344 · Cited by 56
Data work in context: Value, risks, and governance
Foster, J., McLeod, J., Nolin, J., & Greifeneder, E. (2018) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24105 · Cited by 40
Krouska, A., Troussas, C., & Virvou, M. (2019) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12330 · Cited by 41
Influence of social software features on the reuse of Business Intelligence reports
Alpar, P., Engler, T. H., & Schulz, M. (2015) · Information Processing & Management · doi:10.1016/j.ipm.2015.01.004 · Cited by 36
Application of Web 2.0 Technologies in E-Government: A United Kingdom Case Study
Sivarajah, U., Irani, Z., & Jones, S. (2014) · 2014 47th Hawaii International Conference on System Sciences · doi:10.1109/hicss.2014.280 · Cited by 25
Improving Integrated Care: Modelling the performance of an online community of practice
Díaz-Chao, Á., Torrent-Sellens, J., Lacasta-Tintorer, D., & Saigí-Rubió, F. (2014) · International Journal of Integrated Care · doi:10.5334/ijic.1200 · Cited by 21
Gendron, M., Mesquita, B., & Barrett, L. F. (2020) · Culture, Mind, and Brain · doi:10.1017/9781108695374.010 · Cited by 20
Metadata
- Authors
- Ennser-Jedenastik, L., Dolezal, M., & Müller, W. C.
- Venue
- Politics & Gender
- Publisher
- Cambridge University Press (CUP)
- Published
- 2016-11-02
- Volume
- 13
- Issue
- 01
- Pages
- 81-106
- ISSN
- 1743-923X
- 1743-9248
- OpenAlex
- W3146248460
- Cited by
- 1,627
- References
- 4
- Type
- report
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
