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Record W2799805194 · doi:10.1177/1206331218773671

Staging Atmosphere on the Ukrainian Maidan

2018· article· en· W2799805194 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSpace and Culture · 2018
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsMacEwan University
FundersMacEwan University
KeywordsAtmosphere (unit)FeelingUkrainianSociologyPhilosophyPhysicsEpistemologyMeteorologyLinguistics

Abstract

fetched live from OpenAlex

This article uses atmosphere theory to describe the revolutionary events on Ukraine’s Maidan Nezalezhnosti as they unfolded from November 2013 to February 2014. Like other recent occupation movements (Tahrir Square, Gezi Park, Zuccotti Park), the Maidan protestors created a vast infrastructure that supported large-scale protest and daily life on the square. I argue that atmosphere, or the feeling of place, was important to the makeup of Maidan. Like other occupation movements, Maidan became a “world” unto itself because it generated unique feelings that held the place together. Drawing on atmosphere theorists Peter Sloterdijk and Gernot Böhme, I describe the atmospheres of Maidan, show how these atmospheres were generated, and then describe how these atmospheres influenced the course of the revolution.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.025
GPT teacher head0.296
Teacher spread0.271 · 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