Descriptive or Divisive? A Critical Review of Scholarly Perspectives toward Monosexism
Bibliographic record
Abstract
Dichotomous views of gender and sexuality primarily categorize people according to opposing binaries – either those attracted to the ‘opposite’ sex (heterosexual people) or those attracted to the same sex (gay or lesbian people). This ‘either/or’ notion greatly stigmatizes people who exist outside of this binarized categorization due to their attraction, or lack thereof, to multiple gender identities as well as the non-binary nature of their own genders. Past scholars have coined the term ‘monosexism’ to describe the social and cultural structures that operate on the premise that all individuals experience attractions, and that all are (and should be) attracted to no more than one gender (Eisner, Citation2013; Meyer, Citation2019; Roberts et al., Citation2015). Hence, asexual, bisexual, pansexual, and other plurisexual people experience oppression rooted in the normativity of monosexuality, while heterosexual people, lesbians, and gay men–all who adhere to the societal norm of monosexuality– receive privileges. While the impacts of monosexism are evident in the literature (Kaysen et al., Citation2011; Walters et al., Citation2013), there are scholars (Hemmings & Blumenfeld, Citation1996; Weiss, Citation2004) who oppose using the term monosexism and its presumed monosexual categorization – denoting it as pejorative and divisive to the 2SLGBTQIA + community as it groups lesbians and gay men together with heterosexual people as the oppressing, monosexual group. With the increasing scholarly focus on representation and recognition of 2SLGBTQIA + individuals and communities, we contend that understanding the complexity of monosexism is important for a more clearly articulated analysis of gender and sexuality-based inequality and experience. This paper provides a critical review of scholarly perspectives discussing monosexism and at the same time, contributes to a fuller understanding of the nature of this system of oppression and discrimination. Overall, we suggest that comprehending monosexism as a neutral term to describe a social structure that oppresses all sexual identities (not just asexual and plurisexual people) can lead to better understanding and potentially to resisting and dismantling this form of oppression.
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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 | 0.021 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.009 | 0.043 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".