Fitzgerald’s The Great Gatsby Between the Film and the Novel: A Corpus-driven Study of Students’ Responses
Bibliographic record
Abstract
It is argued that films are valuable parts of our culture, that they facilitate students’ understanding of novels, that they make classes more interesting, and that they should be integrated into the course materials. In line with this contention, this article attempts to explore the significance of film as an educational tool from the students’ perspectives. Students have been given a question focusing on their impressions about the novel and the film. They have been also given a week’s reprieve to turn in their responses via e-mail so that they can maintain the necessary privacy and answer freely. In this way, they won’t be obligated to choose a given response similar to what occurs in a questionnaire. The discussion demonstrates that most students are not for watching films, and that their responses are indicative of their uncritical viewing of the film. Having no clear idea about cinematic techniques and directors’ treatment of texts and changing them into scripts, students are not in a position to evaluate films well. Key words: Albinism (albino); Race; Blackness; Community; John Edgar Wideman
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".