<i>Queering the Map</i> : Stories of love, loss and (be)longing within a digital cartographic archive
Bibliographic record
Abstract
The rise of crowdsourced and participatory digital platforms which aim to make visible the experiences of otherwise marginalised people are significant within the broader landscape of digitally mediated community spaces. One example of such media is Queering the Map , a digital storymapping platform where users anonymously pin ‘queer moments’ and memories to places. While the mediation of affect and intimacy in digital spaces among queer people is increasingly attended to in scholarly work, the cartographic and archival remains hitherto underexplored. Drawing on an analysis of almost 2000 micro-stories geolocated to Australia, in this article we explore various aspects of story contribution that situate Queering the Map as a lively cartographic archive. Rather than necessarily anonymous (as the platform dictates), the posts, we argue, entail various deliberated directions or gestures, encoded for audiences: what we term stories for someone . We highlight these publicly private stories’ connective and affective underpinnings, and the political potentialities (and problems) therein for queer belonging and community-building. In doing so we seek to contribute to scholarship on digital archives, crowdsourcing, and advance conceptualisations of digital intimacies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".