The Politician/Celebrity and Fan(Girl) Pleasure: The Line Between Queen Hillary and Presidential Candidate Clinton
Bibliographic record
Abstract
Whenever there is a major political event and the #TheBachelor live-tweeting continues, or popular online media outlets such as Jezebel go ahead with their pre-planned celebrity gossip coverage, there is outrage: seemingly, it is impossible to keep up with—and care about—both the Kardashians and election campaigns. During the 2016 United States’ election, however, this outrage emerged from within campaign coverage, drawing a line between “serious political supporter” (who is interested in facts and policy) and “emotional fangirl” (who is interested in memes, feelings, and “girl power” above all). Despite Donald Trump’s history of reality TV and non-political celebrity, Hillary Clinton’s supporters were called “fangirls” and accused of celebrity-worship, of solely getting their news from “pop” media like BuzzFeed—where foreign policy coverage is found alongside discussions of how “dead” we are from a Clinton eye-roll—and of allowing fandom to cloud political judgment. This paper is not engaging in the “fake news” debate; rather, this paper explores the intersection of political celebrity and politician in a moment when governmental politics, celebrity, social media, and reality TV are overlapping in unprecedented ways, as well as the intersection of “serious” political campaigning and fannish pleasure in an historic moment for women in American politics.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.012 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".