Potential and Hypothesized Determinants of the Positive and Negative Effects as a Result of Pornography Use in Dyadic Relationships
Bibliographic record
Abstract
In this literature review, I will be looking at the different potential and hypnotized determinants of positive and negative effects pornography has on dyadic relationships. Specifically, I recognize three of the most frequently mentioned and studied areas of concern that are discussed in the body of literature. These three areas are: attitudes towards pornography, partnered, solitary and/or non-existent pornography use, and frequency of pornography use. A plethora of mixed effects are found, both positive and negative, and suggestions for future research is made. Theses suggestions include three additional areas to focus on, which include: content of pornography, transparency of pornography use between partners, a more in-depth look at different relationship commitment levels, and how all three of these relate to positive and negative impacts on a dyadic relationship.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".