A Narrative Investigation of Asexual Identity Development, Content, and Psychosocial Functioning
Bibliographic record
Abstract
Though research on asexual orientation emerged two decades ago, only a relatively small amount of research exists. This minimal extant research has primarily focused on fundamental issues (e.g., developing an accurate definition of asexuality and estimating the prevalence of asexual people in the population), with a limited number of studies only recently beginning to explore the psychosocial experiences of asexual people. I contribute to this nascent area of sexuality research by studying asexual identity development and applying a narrative identity framework. This framework proposes that one’s identity takes the form of a life story composed of several important self-defining memories (SDMs). A robust body of research has documented positive associations between psychosocial functioning and SDM dimensions. Informed by this literature, the current study asks three questions: (a) How are narrative dimensions of SDMs (i.e., coherence, affective tone, and meaning-making) related to psychosocial functioning among asexual people?; (b) What types of SDM events constitute the content of asexual identity?; and (c) In what ways do these SDM event types vary from one another? Participants included 370 self-identified asexual people ranging from 18 to 72 years of age (M = 25, SD = 8). Each participant provided a written narrative of an SDM that they considered essential to their asexual identity. SDM narratives were inductively coded for event-type content and deductively coded for narrative coherence, affective tone, and meaning-making using established coding protocols. Participants also completed several measures of psychosocial functioning. In terms of results, first, consistent with the broader narrative identity literature, I found that psychosocial functioning was best among those participants whose SDM narratives were coherent, positive in affective tone, and contained deep self-reflective meaning. Second, I discovered that asexual people’s SDMs tended to focus on six main types of events: (a) navigating allonormative exposures; (b) romantic/intimate experiences; (c) learning about asexuality; (d) sexual experiences; (e) discrimination; and (f) coming out to others as asexual. Third, narratives from the learning about asexuality category tended to be the most positive in affective tone, experienced most positively by the participant (both at the time of the event and in the present recalling the event), and were rated highest for impact on the participant’s life and importance to their asexual identity. This study is the first to examine the content of asexual people’s SDMs, contributing to increased visibility, awareness, and understanding of asexual identity development. The findings of this study are not only significant in their contribution to a growing knowledge base about asexuality but also could be practically helpful to clinicians and service providers working with asexual people.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".