Porn Tube sites: How do gratifications, interactivity and contextual age predict usage and addiction in India?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it