Mapping the social implications of platform algorithms for LGBTQ+ communities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it