City Twinning from a Grassroots Perspective: Introducing a Spatial Framework to the Study of Twin Cities
Bibliographic record
Abstract
This article formulates a conceptual framework to analyze city twinning from the perspective of local inhabitants and applies it to the twin city of Imatra and Svetogorsk on the Finnish–Russian border. This “spatial framework” is inspired by Henri Lefebvre’s spatial triad, which distinguishes between “perceived space,” “conceived space,” and “lived space.” These concepts are utilized to scrutinize the relationship between the concept of a twin city and the everyday life of the inhabitants. Thirty-seven inhabitants from Imatra and Svetogorsk participated in one of six focus groups discussing their life in the cities and the concept of a twin city. The present study indicates that individuals are likely to identify with the twin city if their spatial perceptions of and lived experiences in the twin city correspond with the associations they have of the concept. The article argues that paying more attention to how local citizens understand twin cities as concepts and as spaces for everyday lives contributes to unpacking the phenomenon of city twinning. This research approach—the spatial framework—is not limited to the study of city twinning but can be applied to cross-border region building in general.
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.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.011 |
| 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".