'IF WE LOOK AT IT FROM AN LGBT POINT OF VIEW…’ MOBILIZING LGBTQ+ STAKEHOLDERS TO QUEER ALGORITHMIC IMAGINARIES
Bibliographic record
Abstract
This paper presents the results of an exploratory study that examines the social implications that platform algorithms raise for LGBTQ+ communities. We share the preliminary results of our Phase 2 group interviews, which were conducted with Canadian social media managers of LGBTQ+ non-profit organizations and with Canada-based LGBTQ+ tech workers. Algorithmic controversies relating to LGBTQ+ communities identified in Phase 1 were used as prompts to elicit discussions among participants. In this paper, we pay close attention to how participants queered dominant algorithmic imaginaries. Our preliminary analysis highlights four main findings. First, participants questioned dominant discourses that depict AI technology as being inherently new, instead re-inscribing algorithmic controversies within a long-lasting history of gender and sexual oppression. Second, participants reconfigured the ideal-type user embedded in sociotechnical systems but also identified challenges with effecting sociotechnical change as LGBTQ+ stakeholders. Third, participants subverted the notion of algorithmic resistance by questioning whether effective technological resistance should rely on technological misuse or disuse. Fourth, participants translated algorithmic controversies via their positionality as LGBTQ+ stakeholders to move beyond purely technicist considerations. Finally, we highlight the importance of mobilizing stakeholders from marginalized communities to contest the dominant discourses through which society makes sense of AI technologies.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".