Bibliographic record
Abstract
The famous movie The Great Gatsby ever creates a peak in movies world. Whether the famous stars or the people’s make-ups in the movie are the forever attraction for the watchers. It is one world-known great work and wins many prizes in the world. In the movie there are two main male characters and three female characters and misogyny is shown off through the words and plots. This paper tries to analyze the images of the three main female characters and the narrator Nick’s prejudice against women to explore misogyny embodied in the work. In Nick’s description, Daisy is empty and indifferent, indulged in material life; Jordan is a golf player, a new female, but she deceives more and benefits herself first; Myrtle is the label of vanity and ridiculousness, the ugly and dark image in materialistic society. In fact, Nick’s has double moral standards. In his eyes, the females are lure, sexy and pump and depending on the man while the males are the representative of power, strong and tough. The female characters are arranged many miserable punishments for their breed and vanity, such as some being killed in accidents with miserable ending, some abandoned by men and some losing her real lover. But the man’s bad behaviors receive less punishment, such as Tom betraying Daisy and playing Myrtle’s love.
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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 | 0.000 | 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 | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".