Tensions and Ambivalences of Pride Politics in Uncertain Times
Bibliographic record
Abstract
SEPTEMBER 2018 we travelled to Kirkenes, a town on the Norwegian-Russian border, to attend the Barents Pride organized in collaboration between LGBTI+ activists from Russia and Norway (Liinason and Sasunkevich 2022).This Pride differed from what we knew about world famous Prides in cosmopolitan cities across the world, such as Sydney (Markwell 2022), Toronto (Kates & Belk 2021), or Johannesburg (Conway 2022).Kirkenes is a small, almost rural, Norwegian town on the coast of the Barents Sea, thirty kilometres from the border with Russia.Late September was sunny but crisp there.In line with dominant metronormative assumptions about Prides (Wasshede 2021), we thought it was an unconventional location for Pride activities: too small, too distant, and too cold.The Barents Pride was initiated in 2017 by LGBTI+ activists from Murmansk, a city in the Russian North-West.The Barents Pride was on the one hand a response to increasing state homophobia and violence against LGBTI+ people in Russia, on the other -being organized through a cross-border collaboration between Norwegian and Russian LGBTI+ organizations -a sign of mobilization and transnational solidarity among LGBTI+ activists.This special issue, and our general interest in Pride politics, partly stems from our attempt to grasp and conceptualize the ambivalences of the Barents
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.033 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".