Urban Everyday Life and Electrification in Context of Fuel Crises in First Quarter of 20th Century
Bibliographic record
Abstract
The article examines the social response to the introduction of new electric technologies (street lighting and trams) in cities, as well as the limitations on their use during periods of fuel crises. The concepts of “energy transition” and “shift towards consumerism” serve as the methodological basis for the study. The author relies on L.B. Kafengauz’s periodization when analyzing changes in cities’ electricity supply. The research shows that the new electric infrastructure was in demand among city dwellers. The adaptability of power plants to fuel shortages allowed for the maintenance of electricity supply during crisis years. For the first time, city residents faced a shortage of fuel for power plants during the First World War and the Civil War. The fuel crisis of 1901-1908 went unnoticed by urban populations, as power plant operations were more often disrupted due to worker strikes than fuel shortages. The article discusses the public debate that erupted in Moscow in 1909 regarding the acceptability of allowing tram traffic on Red Square. It also reports on the discussion in contemporary periodicals of the phenomenon of “tramvayizatsiya literatury” (the “tramwayization” of literature, “the literature for trams”): more than half of the passengers on tram cars were reading newspapers or books. The author has gathered interesting materials on worker strikes at tram depots, dissatisfaction among passengers with sharp hat pins on women’s hats, and other social issues that arose as a result of electrifying city life during the specified period.
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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 | 0.001 | 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 | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".