Mapping the social implications of platform algorithms for LGBTQ+ communities
Bibliographic record
Abstract
LGBTQ+ communities were among the first to appropriate the Internet to experiment with their identities and socialize outside of mainstream society. Recently, those platforms have implemented algorithmic systems that curate, exploit, and predict user practices and identities. Yet, the social implications that platform algorithms raise for LGBTQ+ communities remain largely unexplored. At the intersection of media and communication studies, science and technology studies, as well as gender and sexuality studies, this paper maps the main issues that platform algorithms raise for LGBTQ+ users and analyzes their implications for social justice and equity. To do so, it identifies and discusses public controversies through a review and analysis of journalistic articles. Our analysis points to five important algorithmic issues that affect the lives of LGBTQ+ users in ways that require additional scrutiny from researchers, policymakers, and tech developers alike: the ability for sorting algorithms to identify, categorize, and predict the sexual orientation and/or gender identity of users; the role that recommendation algorithms play in mediating LGBTQ+ identities, kinship, and cultures; the development of automated anti-LGBTQ+ speech detection/filtering software and the collateral harm caused to LGBTQ+ users; the power struggles over the nature and effects of visibility afforded to LGBTQ+ issues/people online; and the overall enactment of cisheteronormative biases through platform affordances.
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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 | 0.016 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".