Bibliographic record
Abstract
This Major Research paper will focus on the Republican American politician Sarah Palin. Sarah Palin is a political figure who has played an interesting role in Republican politics over the last four years. As an unexpected candidate for John McCain’s 2008 Vice Presidential nomination, Palin garnered unprecedented media attention for a running mate. Sarah Palin is a media celebrity, a potential Republican candidate for the 2012 election, and an international household name. The purpose of this research is to explore Sarah Palin as a political actor and celebrity icon by analyzing her use of new media as a platform for her political rhetoric. Specifically, this study looks at the discourse used in Sarah Palin’s social media campaign, with a direct focus on the social media outlet of Facebook. Facebook is a non-traditional political media platform, which allows politicians contact with millions of users in a format that is social, personal and direct. Many politicians have been utilizing new media platforms in order to communicate their political messages to new and diverse audiences. This study analyzes how Sarah Palin is utilizing the medium of Facebook, and how the language she uses in communicating to her supporters affects their experience of current political events. This study aims to show the relationship between the rhetoric she chooses to employ, and the comment activity of her supporters on Facebook. Selections of Sarah Palin’s Facebook Note documents were chosen in order to narrow the scope of this research. The research questions that has directed this study is: Through the social media platform of Facebook, what function does Palin’s use of metaphor play in the reciprocal discourse of supporter comments? Do literary devices such as metaphor affect the nature of audience participation in political social media?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".