The Study of the Changes in English Use through the Movie Dialogues in "Pride and Prejudice"
Bibliographic record
Abstract
Language is by no means in a static condition. It changes all the time. The close relation between language and culture makes the changes of a society can be reflected in its language. English writer Jane Austin’s novel Pride and Prejudice has been adapted into movie twice in the year of 1940 and 2005. Characters’ dialogues in the two movie versions are the subject of this research. In this thesis, characters’ dialogues in the two movies are compiled into two copra (Corpus 1940 and Corpus 2005). The relevant theories of sociolinguistics and pragmatics are employed to compare and analyze the differences in language in the 1940 version and 2005 version. The research findings offer suggestions to English language teaching and learning. Most English learners learn English for communicating, and they communicate with modern English speakers, so it’s important for English teachers and learners to realize that what they teach and learn should be modern language which is changing all the time, and their language teaching and learning activities should always keep pace with the constant change of language to avoid being out of date.
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 | 0.001 | 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 | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".