Outing Biology: Finding a Place for the Natural Sciences in Queer Discourses
Bibliographic record
Abstract
This paper critically responds to Stacy Alaimo’s “Eluding Capture: The Science, Culture and Pleasure of Queer Animals” (2010), from Queer Ecologies by Bruce Erickson and Catrina Mortimer-Sandilands. Here, I focus on how the author addresses the relationship between social sciences and natural sciences, how social structures impact the ways in which we understand and interpret scientific data, and how she suggests we embrace the concept of “Naturecultures” in order to move forward in recognizing that heteronormative accounts of life, while dominant, are not the only possible lenses through which nature and sex can/should be seen. I explore Alaimo’s arguments against various different accounts of “same-sex” sexual activity in nature, whilst also reiterating that she does not wish to use animal sex as a form of validation for the LGBTQ+ community, reducing its mere existance to that of biological essentialism and erasing any possible discussions of gender/sexual fluidity by doing so. Instead, she cleverly uses rhetoric regarding animal sex and their perceived sexuality to expose the intrinsic heteronormativity that permeates even the supposedly “empirical” biological sciences, whilst bringing forward what I perceive as a very valuable discussion regarding how social life influences biological life, as opposed to the other way around. Keywords: naturecultures, biopolitics, sexuality, queer
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.077 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| 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".