Bibliographic record
Abstract
Progress in understanding sexual function and dysfunction requires a continued commitment to research and a hefty dose of patience and creativity. We must balance the simplicity of DSM-like definitions of normal and abnormal arousal, desire, performance, and orgasm, with a bewildering array of exceptions to those definitions that characterize sexual function and dysfunction in real people. All too often our questions are obscured by scientific status quo, constrained by research review committees, and have ethical limitations imposed by institutional review boards or government agencies pressured to enforce “community standards” or that are downright hostile to the concept of “lifestyle” improvement where sexuality is concerned. There is much that we simply cannot do either because of ethical considerations, impracticality, or the lack of sufficient technology. This is most obvious when we ask questions about the neurobiology of sexual behavior. Although we can view human brain activation in sexual circumstances or ask questions about the sexual functioning of individuals with specified brain damage or following drug treatments, it is difficult to study those phenomena experimentally. Most people will not knowingly allow themselves to become sexually dysfunctional by an experimental manipulation, and institutional review boards generally do not look favorably on research that monitors human copulatory behavior firsthand. Yet despite the roadblocks, we have made real progress in the past decade in understanding the neuroanatomical and neurochemical mechanisms of various sexual responses such as erection, ejaculation, solicitation, in understanding how they might go awry, and in the design of pharmacological treatments for certain sexual dysfunctions. We have begun to examine the mechanisms that underlie desire, and how sexual stimulation and reward impact on desire, attractiveness, and mate choice. Progress in these areas could not have been made without the help of animal models.
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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.160 | 0.114 |
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".