Examine How Content-unaware Attributes Influencing the Video Views of YouTuber
Bibliographic record
Abstract
YouTube is one of the most popular apps worldwide, and many people create channels to earn income as YouTubers. This research focuses on identifying content-unrelated attributes that impact video views and examining their influence. Initially, data cleaning is conducted to remove missing observations that are not useful for model fitting. Data exploration is performed to gain a basic understanding of the relationships between variables and the response, which may aid in variable selection and model construction. Linear regression models are then fitted to predict video views based on content-unrelated variables. In this paper, these models are compared to select the best one, followed by diagnostics using AIC, R-squared, and MSE. Additionally, the selected model is evaluated for its fit by checking linear assumptions, the presence of outliers, and the significance of predictors. Finally, potential drawbacks of the methods used in this research are discussed, along with consideration of future research directions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".