A Narrative and Cinematic Analysis of Two Film Trailers: Jurassic Park (1993) and Jurassic World (2015)
Bibliographic record
Abstract
This study explores the narrative elements of film trailers to help understand their role and purpose within the marketability of trailers. Current literature from Kernan (2004) focuses on the evolution and standing of trailers as the primary marketing and promotional tool within the film industry. However, this major research paper (MRP) focuses on developing an understanding of the function of the narrative within a film trailer and how this impacts its marketability. More specifically, this MRP provides an analysis of the narrative and film techniques used in both the Jurassic Park (1993) and Jurassic World (2015) film trailers. This study was conducted through a qualitative research methodology primarily using Branigan’s (1992) Narrative Schema and Bordwell & Thompson’s (2008) cinematic framework to provide a thorough analysis of the narrative structure of both trailers. The results of this MRP indicate that the purpose of the of the narrative elements of film trailers is to create an emotional and lasting connection with the audience. Furthermore, results show that over the last twenty years, the narrative elements in successful and marketable film trailers continue to evolve and are becoming increasingly complex and sophisticated given today’s technological advancements. The narrative elements combined with the cinematic techniques are designed to heighten or intensify the audience’s emotional experience so that consumers will be more likely to view the movie in theatres.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".