Bibliographic record
Abstract
The one hundred people shot dead on the Maidan were given the collective name Heavenly Hundred (Nebesna Sotnia). It became the central memory of the uprising; a hymn, a new state award, a national memorial day, poetry, monuments, memorial plaques and books were produced. Dozens of streets and squares were renamed in different regions. The paper focuses on the interpretations of large-scale historical events (the Cossack, the Ukrainian National Republic and World War II), and their incorporation into a new institutionalized narrative after drastic societal events on the example of the protests in Ukraine known as the Maidan in the winter of 2014. The research is based on original protest lore, 8905 Facebook posts from 1647 individuals, collected by the author on the day they were published on Facebook between January 19 – February 28, 2014. This timeframe includes both peaceful days and the most dramatic confrontation of the protests. The data originally was organized in 5 categories and 16 topics. The paper provides evidence of how personal stories function and validate the participants’ experiences and the significance of the events from the protestors’ perspectives; and protest lore impact on institutional changes of commemorative practices in the field of collective memory.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".