Bibliographic record
Abstract
This essay focuses on the issue of male rape. It is recognised that some parts of the essay contain sensitive information in which certain individuals may find disturbing or discomforting. Male rape is a significant social and legal issue. Male victims of sexual offences were traditionally not given equal protection under statute as compared to their female counterparts. Although significant improvements have been made in the Sexual Offences Act 2003, there is still room for improvement. Inspiration can be sought from other common law jurisdictions, namely Australia and Canada, to see how current rape law can be improved. Judicial interpretation, however, also has to be improved to give effect to the strides made in statutory law. There remain significant misunderstandings in male biology, particularly the processes of erection and ejaculation during sexual assault, which can be traced to traditional ideals of masculinity. This can be resolved by adopting modern medical understanding of male physiology. If such problems in the current law are not recognised and rectified, it is submitted that serious ramifications may arise in relation to further stigmatisation of male victims and increased acquittals of dangerous sex offenders. Only by putting male and female victims of sexual offences on equal footing can true gender equality be realised.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".