Bibliographic record
Abstract
Inquiring into human sexuality is difficult. Aside from investigations into the physiology of sex, some of the difficulty is due to work that requires venturing into personal—if not extremely intimate and, possibly, sensitive—areas; and this is the case whether the investigator is a clinician, researcher, or a theorist. Another obstacle to progress in sexuality research is related to the many diverse fields in which investigators receive their training. Each of the many fields spanning the humanities, social sciences, medical sciences, and natural sciences has a unique take on training in technique, methodology, and theory. In particular, terminologies, and understandings of common language, can be unique, and can impact conceptual clarity. Clarity, or at least consistency, is certainly lacking even concerning a common terminology, as Kauth (2005) and others have noted. The lack of clarity is a problem that might prove intractable for a number of reasons beyond the diverse backgrounds of professionals interested in sex, but it is not alone there. Other issues in sexology that remain very difficult, if not impossible, to overcome include institutional, community, sexual, and personal politics that seem to cloud every discussion. Bringing everyone who works on sexuality together under any banner is a daunting, if not impossible, project.
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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.134 | 0.052 |
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".