Bibliographic record
Abstract
In this study, I examine how congressional candidates present gendered identities on their campaign websites. In my theory of candidate gendered identity, drawn from literature on presentation of the self and gender performativity, I argue that candidates construct their personal identities in relation to universally understood archetypes, which stand for ideal representations of real-world characters or roles. Through an in-depth content analysis of the biographical pages of 2010 U.S. House of Representatives candidate campaign websites, I examine how candidates construct and perform a range of gender-based archetypal roles in various electoral contexts. Specifically, I look at how such factors as electoral context, candidate partisan identification, and incumbency status (or challenger status) determine the range of archetypal roles a candidate might choose to perform. What I find is that candidate gender matters, but only for some candidates in some contexts. For many candidates, these factors have an interacting effect on the manner in which a candidate presents his or her gender-based identity. This study contributes to our current understanding of how political candidates behave and present themselves in their political campaigns. In their efforts to connect with and gain the trust of potential voters, candidates present their personal identities through the performances of familiar archetypes with which those voters can easily identify.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".