Porn Tube sites: How do gratifications, interactivity and contextual age predict usage and addiction in India?
Bibliographic record
Abstract
The advent of the internet and compact and compatible smartphones have led to a dramatic increase in the usage of Porntube sites across the globe. Guided by the uses and gratification theory, this study ( N =405) identified six gratifications obtained from tube site usage: Excitement seeking, Diversion, Fantasy, Arousal, Habitual pastime, and Information seeking. This research also located the relationship between gratifications obtained from porn tube sites, life position indicators, interactivity, and problematic usage. Some of the prominent findings of the study are: there are significant age and gender differences in tube sites' usage; life satisfaction negatively predicted tube sites' usage; excitement seeking, diversion, arousal, and habitual pastime gratifications positively predicted porn tube usage; age, gender and interactivity were positive predictors of addiction; excitement seeking arousal, and habitual pastime gratifications positively predicted tube sites' addiction.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".