Bibliographic record
Abstract
As the rapid development of the economy in this era, in response to the needs of people in modern society, people are also demanding more and more movies. the film industry is also in constant development. Firstly, summarizing the results of other domestic and foreign scholars' research on the analysis of factors influencing box office revenues by reviewing national and international academic websites for information related to the topic of this paper. Afterwards, in this paper, analysis of the factors that influence movie revenue with respect to the data set on movies provided by Kaggle website which contains the information of more than 10,000 movies. By choosing the characteristics from the data set which are revenue, popularity, runtime and vote average followed by performing visual analysis showing the relationship between the response variables and independent variables and building the linear regression model. The result shows that the popularity, budget and vote average have significant effects on the revenue. Finally, the suggestions are made on the factors influencing each film's box office revenue and also expressed positive thoughts on the future development of the film industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".