Bibliographic record
Abstract
Background: One of the social groups that influence the activation of the city to the greatest extent are young people, including students. Social spaces dedicated to this group are to embellish the city, have a positive effect on its image, but they are also to be useful. Methods: This paper has been written on the basis of the Author’s study devoted to finding an answer to the question how much a social group that uses a specific space influences the activation of this space. The examined social group were young people, students from two cities: Toronto and Cracow. The spaces used in the study are places separated from school and university buildings, intended for individual study for high school and university students. Results: The results of the study indicate that one of the important factors that according to young people studying in the cities improve the quality of the social space is the existence of legibly marked places intended for individual study, that is places where students can study and spend time after their classes and lectures. Such places animate and activate the space connected with them. Conclusion: The social group of young people who still attend schools and universities constitutes a very important factor of the activation of cities. Providing young people with an attractive offer connected with their individual education has an invigorating effect on the city.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".