Queering the System from within: Autostraddle as a Method for Future Digital Worlds
Bibliographic record
Abstract
Despite the harmful potentials of social media, the digital world appears to offer endless potential for change. A queer and feminist example is Autostraddle, a digital community and publication for LGBTQIA2s + people run by feminist queer and trans folx, that attempts to mitigate the potential harms of social media platforms while existing within and beyond its borders. Situating Autostraddle within the larger context of social media platforms and feminist communities online, this article considers how Autostraddle’s original model queers the system from within (Tsika, Noah. 2016a. “CompuQueer: Protocological Constraints, Algorithmic Streamlining, and the Search for Queer Methods Online.” Women’s Studies Quarterly 44 (3/4): 111–130), creatively reworks corporate platforms (Trott, Verity Anne. 2023. Feminist Activism and Platform Politics. E-Book: Abingdon: Routledge), and designs social media for difference (McPherson, Tara. 2014. “Designing for Difference.” Differences 25 (1): 177–188). Autostraddle makes use of the following queer methods to reconstruct the digital landscape for queer humans: Queer reversal and the two T’s (transparency and transformation). Through employing practices such as placing an emphasis on community and a culture of care, enacting transparency to make the invisible visible, and implementing data policies and safety practices that prioritise users over profits, Autostraddle contributes to critical reimaginings for the future. Examining Autostraddle’s methods demonstrates one approach for incorporating feminist and queer theory to (re)envision a more equitable digital future.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.052 |
| Scholarly communication | 0.014 | 0.027 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".