A Psychometric Investigation of the Consumption of Pornography Scale (COPS-G) in Undergraduate Students at a Private Christian University
Bibliographic record
Abstract
Pornography is a multidimensional and complex topic that provokes feelings of shame, curiosity, and anxiety. In recent times, pornographic searches on the internet amounted to 25% of total search engine requests (Cooper et al., 2000). In a Canadian study on college students’ problematic pornography use, a higher level of pornography use was associated with negative psychosocial functioning and maladaptive use of cannabis, alcohol, gambling, and video games (Harper & Hodgins, 2016). Many researchers have attempted to mitigate the inconsistent measurement of pornography usage (Mckee et al., 2020; Short et al., 2012). Hatch et al. (2020) addressed these measurement issues and developed a pornography usage measure that focuses on behavior rather than subjective perception. The goal of the current study is to further evaluate the psychometric functioning of the COPS-G in a novel sample of Christian undergraduate students. We hypothesize that the COPS-G will demonstrate: (a) adequate internal consistency; (b) convergent validity; and (c) structural validity. Participants consisted of a convenience sample of undergraduate students from a private, Christian university in the Pacific Northwest. The COPSG subscales in this study demonstrated adequate internal consistency within the whole sample. The COPS-G subscales demonstrated mixed support for convergent validity. For structural validity, the three-factor models performed adequately, with the bifactor model performing the best. The present study adds to the complexity of this research and also provides some directionality for future research in regard to the behavioral elements of pornography use.
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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 | 0.003 | 0.007 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".