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Record W3091903642 · doi:10.5210/spir.v2020i0.11319

QUEERING THE MAP: PHYSICAL TRACES AND DIGITAL PLACES OF QUEERLIVES

2020· article· en· W3091903642 on OpenAlexaff
Brady Robards, Ash Watson, Emma Kirby, Brendan Churchill, Lucas LaRochelle

Bibliographic record

VenueAoIR Selected Papers of Internet Research · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsConcordia University
Fundersnot available
KeywordsQueerPrideLesbianTransgenderNarrativeGender studiesSociologyContext (archaeology)StorytellingAestheticsVisual artsArtHistoryLiteraturePolitical scienceLaw

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.670
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.337
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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