Bibliographic record
Abstract
This blog-style paper written for PSYC333 concerns the rampant misinformation regarding the safe and healthy sexual practices of masturbation, informed by research regarding masculinity, masturbation, and health. In today's society, especially amongst adolescents and young adults, the idea of “No Nut November,” amongst other trends where masturbation is discouraged, runs rampant and is a very common source of this misinformation. In this paper, the problematic roots of No Nut November are addressed, and the broader harmful implications of this misleading information are discussed. Previous research done regarding abstaining from masturbation is reviewed to demonstrate that masturbation has been empirically proven to have great health benefits, and little to no disadvantages. Research regarding the clinical benefits of abstaining from masturbation, is reviewed to illustrate that there have been no empirical findings to indicate that any significant clinical benefits can be yielded from abstaining from masturbation. The paper discusses the implications of these findings; shaming people for these healthy and natural sexual behaviors contributes to severe emotional and physical harm-especially when it concerns adolescents. This is a narrative that needs to be recognized as more than an internet joke, especially on campus; the first step to combatting it is to educate those most susceptible to harm.
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.
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.110 | 0.026 |
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".