Bibliographic record
Abstract
This article approaches the concept of the “vitality of local communities” and the possibilities of its measurement. It describes the efforts of researchers to define vitality at the level of regions, cities, or even neighborhoods. With the advent of new technologies, we are experiencing an increase in possibilities and tools to visualize vitality in the form of maps or interactive databases. We are witness to numerous projects that explore the vitality of communities in the USA and Canada, but this approach has penetrated the rest of the world only to a lesser extent and in a significantly altered form. The article notes a selected parameter – the dwellings that people build, adapt, or abandon. Dwellings are a well-researched feature from various anthropological perspectives even historically. Thanks to this, it is possible to quantify the level of vitality of cities through houses. We have much data from field research in a specific location in South-Central Slovakia. With the help of said data, it will be possible to create a locally adapted tool for measuring the vitality of the local community. It can be used as an indicator of the quality of life in terms of local policy-making, urban planning, or development forecasting. Therefore, it can be an important tool in the study of depopulating regions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
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".