Sexual and geocultural algorithmic imaginaries: Examining approaches of participatory resignation among LGBTQ+ Instagrammers in Berlin and Montreal
Bibliographic record
Abstract
This study builds on theories of user imaginaries by examining how LGBTQ+ creators in Montreal, Canada and Berlin, Germany respond to perceived algorithmic bias. Through observation and close reading of creators’ Instagram content, the study finds that expectations of discrimination based on sexual and gender identity, embedded in geographical and sociocultural contexts, shape these users’ understandings of threats posed by algorithmic governance. Findings also identified three main responses to perceived algorithmic bias: direct calls for engagement, strategies for eluding algorithmic surveillance, and adaptation to presumed algorithmic parameters. Instead of giving up or leaving, these responses demonstrated users’ participatory resignation, as an expectation of algorithmic bias informed by past experiences of identity-based discrimination paired with determination to negotiate such bias to endure on the platform. Thus, this article contributes a novel comparative analysis that expands conceptualizations of algorithmic imaginaries while revealing how resignation is mobilized as resistance to algorithmic governance.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".