A validated measure of no sexual attraction: The Asexuality Identification Scale.
Bibliographic record
Abstract
Human asexuality has been described as a lack of sexual attraction toward anyone or anything. One percent of the adult population is thought to be asexual, and research suggests that asexuality is best conceptualized as a sexual orientation. A serious limitation in past research on asexuality has been the complete lack of a validated tool to measure asexuality. Due to limitations in recruiting sufficiently powered local samples, most studies have relied on recruiting via online web-based asexual communities. This is problematic because it limits the sample to individuals who have been recruited through established asexuality networks/communities. The present study aimed to develop and validate a self-report questionnaire to assess asexuality. The questionnaire was intended to provide a valid measure independent of whether the individual self-identified as asexual and was developed in several stages, including: development and administration of open-ended questions (209 participants: 139 asexual and 70 sexual); administration and analysis of resulting 111 items (917 participants: 165 asexual and 752 sexual); administration and analysis of 37 retained items (1,242 participants: 316 asexual and 926 sexual); and validity analysis of the final items. The resulting Asexuality Identification Scale (AIS), a 12-item questionnaire, is a brief, valid, and reliable self-report instrument for assessing asexuality. It is psychometrically sound, easy to administer, and has demonstrated ability to discriminate between sexual and asexual individuals. It should prove useful to allow researchers to recruit more representative samples of the asexual population, permitting for an increased understanding of asexuality.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".