QUEERING THE MAP: PHYSICAL TRACES AND DIGITAL PLACES OF QUEERLIVES
Bibliographic record
Abstract
QueeringTheMap.com, launched in late 2017 by designer Lucas LaRochelle, is a ‘community-generated mapping project that geo-locates queer moments, memories and histories in relation to physical space’. In a Google Maps-style interface, users can locate pins anchored to physical locations. Attached to each pin is a story. Collectively, there are tens of thousands of stories about being lesbian, gay, bisexual, transgender, non-binary, and gender non-conforming: coming out stories, stories of first kisses, sexual encounters, break-ups, pride marches, assaults, traumas, and realisations. These stories digitally layer physical spaces with anonymous individual and collective stories; they locate queer life and they queer the map. In this project we seek to document these experiences, improve understandings of community archiving and digital storytelling practices, and expose the potential for reconfiguring forms of resistance and solidarity through new platforms for collectivity and community-making. In this paper, we consider how these narratives may be understood at scale to provide insights into the digital architecture of queer lives. We focus our analysis on the 1,941 posts pinned to Australia, to consider how QTM reaffirms contemporary understandings of the physical-digital continuum and queers how we conceive of traces and places in this context.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".