Ethics of extended reality in movie marketing: A utilitarianism approach
Bibliographic record
Abstract
In current times, movie marketing has shifted toward an amalgamation of mechanization and human interactions with the increasing use of immersive technologies like extended reality, which is an umbrella term for all technologies such as augmented reality and virtual reality. Individuals watch movies in their leisure time. Through the use of utilitarianism as a theoretical lens, the study aims to analyze the ethical implications of using extended reality in movie content and promotion, and gauge whether such immersive technologies are beneficial for end-users and marketers. Each of the three axioms of utilitarianism is applied to assess the impacts of extended reality usage in movies. The examples are taken from two relevant industries, Hollywood and Bollywood. The study aims to act as a reference for marketers and academicians who wish to work in the realm of immersive technology usage in the entertainment industry and who wish to analyze its ethical implications.
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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 | 0.034 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.069 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| 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".