Chasing Aces: Asexuality, Misinformation and the Challenges of Identity
Bibliographic record
Abstract
Asexuality is a deeply misunderstood and little-known sexual orientation. This is partly due to misconceptions and marginalization of asexual people, and partly by a lack of information about the orientation. This paper outlines the misconceptions of the ‘causes’ of asexuality, namely Hypoactive Sexual Desire Disorder (HSDD), abuse, and religious abstinence. These causes are shown to be invalid due to the key element of self-identification in determining an orientation.; nevertheless, they persist in society because little is known about the nature of asexuality. The facets of the asexual orientation are then discussed: levels of sexual attraction, sexual desire, and romantic orientation, displaying the complex attempt to define asexuality, made even more difficult by a lack of sources concerning these facets.. Finally, the tension between the LGBTQ+ community and asexuals is discussed in terms of the debate about including asexuals in this community, with the groups often speaking at cross-purposes. It becomes clear that being asexual requires a complex navigation of territory, and this problem is exacerbated at every step by a lack of information. It is therefore crucial that this informational gap is addressed at each of these three critical areas in order to build a more complete societal grasp of asexuality, and to create a vibrant, open community for those who identify as asexual.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.043 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".