Representation of Gender Through Framing: A Critical Discourse Analysis of Hillary Clinton’s Selected Speeches
Bibliographic record
Abstract
Foucault’s theory of power and discourse has opened new horizons in the various fields of linguistics. It has brought the working of the power of discourse into the focus of research. Critical Discourse Analysis looks at this relationship between language and power. Language is taken as a patent tool for exerting power and for building identity (Foucault, 1998). Critical discourse analysis (CDA) reveals the ways by which discourse is manipulated for the construction of various domains such as identity, ethnicity, ideology, cultural differences and gender. The most wide-ranging and most influential work in CDA is of Norman Fairclough. He takes language as a social practice. He makes it clear that the power of discourse is used for depiction of ideology and gender representation. The present study used Critical Discourse Analysis (CDA) as an approach to find out the working of frames for representation of gender identity. The current study analyzed the campaign speeches of Hillary Clinton for finding out her projection of gender identity through frames. The data of the study consists of her opening primary campaign speech which is the Campaign Launch Speech and her last speech for Primary campaign that was delivered in the American presidential election of 2016. The theoretical framework for the present study is Fairclough’s Three Dimensional Model (2015) and the tool applied on this model for looking into the working of frames is the Frame Problem Tool of Gee (2014). The results of the study revealed that Hillary used the technique of framing for projecting her gender identity. She used the fight and family frames for the modification of the boundaries of American presidency with respect to gender. Through her political discourse she framed herself as a brave and bold woman who had she become the president of the United States would have fought for the rights of all Americans irrespective of their creed, sect, religion, gender and nationality.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".