Racial and Gender Discrimination Reflected in Theodore Melfi’s Hidden Figures
Bibliographic record
Abstract
The United States of America has a long history of discrimination based on race and gender. People were divided according to their race, religion, and skin tone. Both black males and their white partners discriminated against African American women. The application of Jim Crow Laws worsened their situation. Both individual and institutional discrimination had to be endured by them. Uncovering the racial and gender inequality faced by African American women is one of the themes explored in Theodore Melfi's Hidden Figure. The study's goals were to look at the fight for equality as a response to the prejudice towards people of color and women that three important female characters in Theodore Melfi's movie Hidden Figures experienced. A book by Margot Lee Shetterly served as the inspiration for the movie. The writers employed sociological, historical, and descriptive analytic methods to analyze this research. Through conducting library research, data was acquired. The main information was taken directly from the film, while the supporting information was gathered from books, magazines, and other sources related to English literature. The collected data were organized and evaluated before being used in the article. This research made it clear to the writers that the movie expressed opposition to discrimination against African American women. The three female protagonists of the movie experienced a variety of sorts of prejudice from their society, both at home and at work. The protagonists struggled with having to create their identities to be accepted by their society, especially by their coworkers at their place of employment who treat them equally and without any discrimination. They eventually won the right to be treated equally and were freed from various types of bias after considerable struggle and self-justification.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".