Gender and Power of Language in A Passage to India by Edward Forster
Bibliographic record
Abstract
In this research, the main issue is to illustrate the huge differences between female and male characters’ choice of words and their linguistic and psychological effect of the novel’s A Passage to India by Forster (1924). The researchers have set some questions and attempted to answer them through using qualitative methods endorsed by Potter's (1999) and Lakoff's (1973). These qualitative methods are the ones which focus on vocabulary, word analysis, and word meaning. The main concern of these methods is to gather non-numerical data proofing our main idea even more by giving examples from the incidents in the novel. They also refer to the meanings, concepts, definitions, characteristics, metaphors, symbols, and description of things. The research comes out with some important findings. It is revealed that words alone do deliver the whole meaning. However, it is demonstrated that gender, body language, words of politeness, and punctuations that show the tone of voice do help words convey their effect more clearly. It is also found that females have strong tendency to use descriptive words to express their feelings. This makes females' language more pleasant than males'. It is further noticed that females use tag questions more commonly to seek approval. On the other hand, it is observed that males produce formal sentences to realize and ascertain dominance in their speech.
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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 | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".