Young Towns: Scaling Sites of Memory
Bibliographic record
Abstract
It has been assumed to regard young towns built during Soviet times as possessing only a “short history”. To deal with the sites of memory of such settlements, an especial research approach is elaborated integrating both theoretical resources of memory studies and scale studies. According to this approach, sites of memory are analyzed as a), in temporal and space coordinates, and b), from the perspective of “ordinary” people. Two groups of scales, 1) worldwide and national, and 2) regional and urban, are considered as the materials of the empirical research in the four young Ural towns of Kachkanar, Krasnoturinsk, Lesnoy, and Zarechny. The main methods of data-gathering were go-along interviewing and photo mapping. The data sources include the archives of the local, regional, and central printed presses, archival documents including minutes of Communist Party meetings, the official website of each town, and others. As our research has shown, the most time-depth, up to centuries and millennia, is characteristic of the sites of memory on a regional scale; in other cases, memory extends no further than the biography of two or three generations. Large scales provide the residents of small settlements with a portal to the big world, helping them to feel a connectedness with other cities and countries. Local-scale sites of memory symbolically unites people in a single community, allowing a shared perception of space and local competence. In conclusion, the analytical traps inherited from the original concepts are discussed as well as the opportunities to overcome them and the prospects for further research, such as the study of scaling as a process, coming from above and below, purposefully and spontaneously, or formally and informally. Of particular interest are the scales intersections, the slip of the sites of memory on the scales, and the fixing by the effects of understatement and exaggeration of scale (scale-ups and scale-downs).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".